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Lixing Zhu

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Guo, X. & McAleer, M.J. & Wong, W.-K. & Zhu, L., 2016. "A Bayesian Approach to Excess Volatility, Short-term Underreaction and Long-term Overreaction during Financial Crises," Econometric Institute Research Papers EI2016-01, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.

    Cited by:

    1. Imran Yousaf & Shoaib Ali & Wing-Keung Wong, 2020. "An Empirical Analysis of the Volatility Spillover Effect between World-Leading and the Asian Stock Markets: Implications for Portfolio Management," JRFM, MDPI, vol. 13(10), pages 1-28, September.
    2. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Management Information, Decision Sciences, and Financial Economics : a connection," Econometric Institute Research Papers 2018-004/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Lin, Edward M.H. & Sun, Edward W. & Yu, Min-Teh, 2020. "Behavioral data-driven analysis with Bayesian method for risk management of financial services," International Journal of Production Economics, Elsevier, vol. 228(C).
    4. Richard Lu & Chen-Chen Yang & Wing-Keung Wong, 2018. "Time Diversification: Perspectives From The Economic Index Of Riskiness," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-15, September.
    5. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Econometric Institute Research Papers EI2018-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Imlak Shaikh, 2019. "Behaviors of Stocks and Fear Index from Terrorist Attacks: Empirical Evidence from SENSEX and NVIX," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 18(2), pages 195-219, September.
    7. Riza Demirer & Rangan Gupta & Zhihui Lv & Wing-Keung Wong, 2018. "Equity Return Dispersion and Stock Market Volatility: Evidence from Multivariate Linear and Nonlinear Causality Tests," Working Papers 201846, University of Pretoria, Department of Economics.
    8. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, And Big Data: Connections," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 36-94, December.
    9. Praveen Kumar Tripathi & Manika Agarwal, 2024. "A Bayes Analysis of Random Walk Model Under Different Error Assumptions," Annals of Data Science, Springer, vol. 11(5), pages 1635-1652, October.
    10. Kim-Hung Pho & Tuan-Kiet Tran & Thi Diem-Chinh Ho & Wing-Keung Wong, 2019. "Optimal Solution Techniques in Decision Sciences A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 114-161, March.
    11. Tareq Almazyad & Norhayati Zakuan & Laith Alrubaiee & Shamaila Butt & Azmirul Ashaari & Raghed IBRAHIM ESMAEEL, 2024. "Bibliometric Insights into Crisis Management: A Review of Key Literature," Advances in Decision Sciences, Asia University, Taiwan, vol. 28(2), pages 1-34, June.
    12. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Tinbergen Institute Discussion Papers 18-024/III, Tinbergen Institute.
    13. Wenjing Xie & João Paulo Vieito & Ephraim Clark & Wing-Keung Wong, 2020. "Could Mergers Become More Sustainable? A Study of the Stock Exchange Mergers of NASDAQ and OMX," Sustainability, MDPI, vol. 12(20), pages 1-25, October.
    14. Nguyen Huu Hau & Tran Trung Tinh & Hoa Anh Tuong & Wing-Keung Wong, 2020. "Review of Matrix Theory with Applications in Education and Decision Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(1), pages 28-69, March.
    15. Li, Si & He, Fangyi & Shi, Fangquan, 2023. "Cognitive biases, downside risk shocks, and stock expected returns," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    16. Kim-Hung Pho & Thi Diem-Chinh Ho & Tuan-Kiet Tran & Wing-Keung Wong, 2019. "Moment Generating Function, Expectation And Variance Of Ubiquitous Distributions With Applications In Decision Sciences: A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(2), pages 65-150, June.

  2. Guo, Xu & Zhu, Xuehu & Wong, Wing-Keung & Zhu, Lixing, 2013. "A Note on Almost Stochastic Dominance," MPRA Paper 44365, University Library of Munich, Germany.

    Cited by:

    1. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "Make Almost Stochastic Dominance really Almost," MPRA Paper 49745, University Library of Munich, Germany.
    2. Jow-Ran Chang & Wei-Han Liu & Mao-Wei Hung, 2019. "Revisiting generalized almost stochastic dominance," Annals of Operations Research, Springer, vol. 281(1), pages 175-192, October.
    3. Lean, H.H. & McAleer, M.J. & Wong, W.-K., 2013. "Risk-averse and Risk-seeking Investor Preferences for Oil Spot and Futures," Econometric Institute Research Papers EI 2013-27, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Jamila Abaidi Hasnaoui & Syed Kumail Abbas Rizvi & Krishna Reddy & Nawazish Mirza & Bushra Naqvi, 2021. "Human capital efficiency, performance, market, and volatility timing of asian equity funds during COVID-19 outbreak," Journal of Asset Management, Palgrave Macmillan, vol. 22(5), pages 360-375, September.
    5. Wing-Keung Wong & Chenghu Ma, 2008. "Preferences over location-scale family," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 37(1), pages 119-146, October.
    6. Thomas C. Chiang & Hooi Hooi Lean & Wing-Keung Wong, 2008. "Do REITs Outperform Stocks and Fixed-Income Assets? New Evidence from Mean-Variance and Stochastic Dominance Approaches," JRFM, MDPI, vol. 1(1), pages 1-40, December.
    7. Hooi Hooi Lean & Michael McAleer & Wing-Keung Wong, 2010. "Investor Preferences for Oil Spot and Futures Based on Mean-Variance and Stochastic Dominance," Working Papers in Economics 10/22, University of Canterbury, Department of Economics and Finance.
    8. Christodoulakis, George & Mohamed, Abdulkadir & Topaloglou, Nikolas, 2018. "Optimal privatization portfolios in the presence of arbitrary risk aversion," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1172-1191.
    9. Buhong Zheng, 2018. "Almost Lorenz dominance," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 51(1), pages 51-63, June.
    10. Liqun Liu & Jack Meyer, 2021. "Stochastic superiority," Journal of Risk and Uncertainty, Springer, vol. 62(3), pages 225-246, June.
    11. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Econometric Institute Research Papers EI2018-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, And Big Data: Connections," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 36-94, December.
    13. Zhidong Bai & Hua Li & Michael McAleer & Wing-Keung Wong, 2015. "Stochastic dominance statistics for risk averters and risk seekers: an analysis of stock preferences for USA and China," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 889-900, May.
    14. Guo, Xu & Post, Thierry & Wong, Wing-Keung & Zhu, Lixing, 2013. "Moment Conditions for Almost Stochastic Dominance," MPRA Paper 51725, University Library of Munich, Germany.
    15. Junová, Jana & Kopa, Miloš, 2025. "Measures of stochastic non-dominance in portfolio optimization," European Journal of Operational Research, Elsevier, vol. 321(1), pages 269-283.
    16. Kim-Hung Pho & Tuan-Kiet Tran & Thi Diem-Chinh Ho & Wing-Keung Wong, 2019. "Optimal Solution Techniques in Decision Sciences A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 114-161, March.
    17. Wong, Wing-Keung & Li, Chi-Kwong, 1999. "A note on convex stochastic dominance," Economics Letters, Elsevier, vol. 62(3), pages 293-300, March.
    18. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2016. "Management Science, Economics and Finance: A Connection," Econometric Institute Research Papers EI2016-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    19. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Tinbergen Institute Discussion Papers 18-024/III, Tinbergen Institute.
    20. Xu, Guo & Wing-Keung, Wong & Lixing, Zhu, 2013. "Almost Stochastic Dominance for Risk-Averse and Risk-Seeking Investors," MPRA Paper 51744, University Library of Munich, Germany.
    21. Denuit, Michel & Huang, Rachel & Tzeng, Larry, 2013. "Almost Expectation and Excess Dependence Notions," LIDAM Discussion Papers ISBA 2013005, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    22. Mirza, Nawazish & Abbas Rizvi, Syed Kumail & Saba, Irum & Naqvi, Bushra & Yarovaya, Larisa, 2022. "The resilience of Islamic equity funds during COVID-19: Evidence from risk adjusted performance, investment styles and volatility timing," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 276-295.
    23. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2016. "Almost stochastic dominance for risk averters and risk seeker," Finance Research Letters, Elsevier, vol. 19(C), pages 15-21.
    24. Nguyen Huu Hau & Tran Trung Tinh & Hoa Anh Tuong & Wing-Keung Wong, 2020. "Review of Matrix Theory with Applications in Education and Decision Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(1), pages 28-69, March.
    25. Kim-Hung Pho & Thi Diem-Chinh Ho & Tuan-Kiet Tran & Wing-Keung Wong, 2019. "Moment Generating Function, Expectation And Variance Of Ubiquitous Distributions With Applications In Decision Sciences: A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(2), pages 65-150, June.
    26. Wong, Wing-Keung, 2007. "Stochastic dominance and mean-variance measures of profit and loss for business planning and investment," European Journal of Operational Research, Elsevier, vol. 182(2), pages 829-843, October.
    27. Wei-Han Liu & Jow-Ran Chang & Guo-Jun Yang, 2024. "An improved criterion for almost marginal conditional stochastic dominance," Review of Quantitative Finance and Accounting, Springer, vol. 62(3), pages 1251-1290, April.
    28. Wing-Keung Wong & Raymond H. Chan, 2005. "Prospect and Markowitz Stochastic Dominance," Monash Economics Working Papers 08/05, Monash University, Department of Economics.

  3. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "Two-moment decision model for location-scale family with background asset," MPRA Paper 43864, University Library of Munich, Germany.

    Cited by:

    1. Moawia Alghalith & Xu Guo & Cuizhen Niu & Wing-Keung Wong, 2017. "Input Demand Under Joint Energy and Output Prices Uncertainties," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(04), pages 1-12, August.

  4. Guo, Xu & Post, Thierry & Wong, Wing-Keung & Zhu, Lixing, 2013. "Moment Conditions for Almost Stochastic Dominance," MPRA Paper 51725, University Library of Munich, Germany.

    Cited by:

    1. Hoang, Thi-Hong-Van & Wong, Wing-Keung & Zhu, Zhenzhen, 2015. "Is gold different for risk-averse and risk-seeking investors? An empirical analysis of the Shanghai Gold Exchange," Economic Modelling, Elsevier, vol. 50(C), pages 200-211.
    2. Zhuo Qiao & Wing-Keung Wong, 2015. "Which is a better investment choice in the Hong Kong residential property market: a big or small property?," Applied Economics, Taylor & Francis Journals, vol. 47(16), pages 1670-1685, April.
    3. Tsang, Chun-Kei & Wong, Wing-Keung & Horowitz, Ira, 2016. "Arbitrage Opportunities, Efficiency, and the Role of Risk Preferences in the Hong Kong Property Market," MPRA Paper 74347, University Library of Munich, Germany.
    4. Tsang, Chun-Kei & Wong, Wing-Keung & Horowitz, Ira, 2016. "A stochastic-dominance approach to determining the optimal home-size purchase: The case of Hong Kong," MPRA Paper 69175, University Library of Munich, Germany.
    5. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Econometric Institute Research Papers EI2018-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    6. Ephraim Clark & Zhuo Qiao & Wing-Keung Wong, 2016. "Theories Of Risk: Testing Investor Behavior On The Taiwan Stock And Stock Index Futures Markets," Economic Inquiry, Western Economic Association International, vol. 54(2), pages 907-924, April.
    7. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, And Big Data: Connections," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 36-94, December.
    8. Abdelbari El Khamlichi & Thi Hong Van Hoang & Wing‐keung Wong, 2016. "Is Gold Different for Islamic and Conventional Portfolios? A Sectorial Analysis," Post-Print hal-02964594, HAL.
    9. Bruni, Renato & Cesarone, Francesco & Scozzari, Andrea & Tardella, Fabio, 2017. "On exact and approximate stochastic dominance strategies for portfolio selection," European Journal of Operational Research, Elsevier, vol. 259(1), pages 322-329.
    10. Kim-Hung Pho & Tuan-Kiet Tran & Thi Diem-Chinh Ho & Wing-Keung Wong, 2019. "Optimal Solution Techniques in Decision Sciences A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 114-161, March.
    11. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2016. "Management Science, Economics and Finance: A Connection," Econometric Institute Research Papers EI2016-26, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Tinbergen Institute Discussion Papers 18-024/III, Tinbergen Institute.
    13. Wing-Keung Wong & Hooi Hooi Lean & Michael McAleer & Feng-Tse Tsai, 2018. "Why Are Warrant Markets Sustained in Taiwan but Not in China?," Sustainability, MDPI, vol. 10(10), pages 1-17, October.
    14. Bouri, Elie & Gupta, Rangan & Wong, Wing-Keung & Zhu, Zhenzhen, 2018. "Is wine a good choice for investment?," Pacific-Basin Finance Journal, Elsevier, vol. 51(C), pages 171-183.
    15. Xu, Guo & Wing-Keung, Wong & Lixing, Zhu, 2013. "Almost Stochastic Dominance for Risk-Averse and Risk-Seeking Investors," MPRA Paper 51744, University Library of Munich, Germany.
    16. Wang, Hongxia & Zhou, Lin & Dai, Peng-Fei & Xiong, Xiong, 2022. "Moment conditions for fractional degree stochastic dominance," Finance Research Letters, Elsevier, vol. 49(C).
    17. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2016. "Almost stochastic dominance for risk averters and risk seeker," Finance Research Letters, Elsevier, vol. 19(C), pages 15-21.
    18. Chan, Raymond H. & Chow, Sheung-Chi & Guo, Xu & Wong, Wing-Keung, 2022. "Central moments, stochastic dominance, moment rule, and diversification with an application," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    19. Nguyen Huu Hau & Tran Trung Tinh & Hoa Anh Tuong & Wing-Keung Wong, 2020. "Review of Matrix Theory with Applications in Education and Decision Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(1), pages 28-69, March.
    20. Kim-Hung Pho & Thi Diem-Chinh Ho & Tuan-Kiet Tran & Wing-Keung Wong, 2019. "Moment Generating Function, Expectation And Variance Of Ubiquitous Distributions With Applications In Decision Sciences: A Review," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(2), pages 65-150, June.
    21. Francesco Cesarone & Justo Puerto, 2024. "New approximate stochastic dominance approaches for Enhanced Indexation models," Papers 2401.12669, arXiv.org.

  5. Alghalith, Moawia & Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "Input Demand under Joint Energy and Output Prices Uncertainties," MPRA Paper 52368, University Library of Munich, Germany.

    Cited by:

    1. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Management Information, Decision Sciences, and Financial Economics : a connection," Econometric Institute Research Papers 2018-004/III, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. Richard Lu & Chen-Chen Yang & Wing-Keung Wong, 2018. "Time Diversification: Perspectives From The Economic Index Of Riskiness," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-15, September.
    3. Chang, C-L. & McAleer, M.J. & Wong, W.-K., 2018. "Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology: Connections," Econometric Institute Research Papers EI2018-08, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Xu Guo & Raymond H. Chan & Wing-Keung Wong & Lixing Zhu, 2019. "Mean–variance, mean–VaR, and mean–CVaR models for portfolio selection with background risk," Risk Management, Palgrave Macmillan, vol. 21(2), pages 73-98, June.
    5. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, And Big Data: Connections," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 36-94, December.
    6. Alghalith, Moawia & Niu, Cuizhen & Wong, Wing-Keung, 2017. "The impacts of joint energy and output prices uncertainties in a mean-variance framework," MPRA Paper 79739, University Library of Munich, Germany.
    7. Chia-Lin Chang & Michael McAleer & Wing-Keung Wong, 2018. "Decision Sciences, Economics, Finance, Business, Computing, and Big Data: Connections," Tinbergen Institute Discussion Papers 18-024/III, Tinbergen Institute.
    8. Subhadip Mukherjee & Soumyatanu Mukherjee & Mamata Parhi & Kun Duan & Ahmed Usman, 2024. "A risk–return trade‐off or co‐movement? Are food processing firms risk‐averse?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 2176-2192, April.
    9. Wenjing Xie & João Paulo Vieito & Ephraim Clark & Wing-Keung Wong, 2020. "Could Mergers Become More Sustainable? A Study of the Stock Exchange Mergers of NASDAQ and OMX," Sustainability, MDPI, vol. 12(20), pages 1-25, October.
    10. Nguyen Huu Hau & Tran Trung Tinh & Hoa Anh Tuong & Wing-Keung Wong, 2020. "Review of Matrix Theory with Applications in Education and Decision Sciences," Advances in Decision Sciences, Asia University, Taiwan, vol. 24(1), pages 28-69, March.

  6. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "Almost Stochastic Dominance and Moments," MPRA Paper 49205, University Library of Munich, Germany.

    Cited by:

    1. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "Make Almost Stochastic Dominance really Almost," MPRA Paper 49745, University Library of Munich, Germany.

  7. Fan, Yan & Härdle, Wolfgang Karl & Wang, Weining & Zhu, Lixing, 2013. "Composite quantile regression for the single-index model," SFB 649 Discussion Papers 2013-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Yazhao Lv & Riquan Zhang & Weihua Zhao & Jicai Liu, 2015. "Quantile regression and variable selection of partial linear single-index model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 375-409, April.
    2. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    3. Jing Sun, 2016. "Composite quantile regression for single-index models with asymmetric errors," Computational Statistics, Springer, vol. 31(1), pages 329-351, March.
    4. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB-Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    5. Yu, Lining & Härdle, Wolfgang Karl & Borke, Lukas & Benschop, Thijs, 2017. "FRM: A financial risk meter based on penalizing tail events occurrence," SFB 649 Discussion Papers 2017-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020. "An AI approach to measuring financial risk," Papers 2009.13222, arXiv.org.

  8. Fan, Yan & Härdle, Wolfgang Karl & Wang, Weining & Zhu, Lixing, 2013. "Composite quantile regression for the single-index model," SFB 649 Discussion Papers 2013-010, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Yazhao Lv & Riquan Zhang & Weihua Zhao & Jicai Liu, 2015. "Quantile regression and variable selection of partial linear single-index model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 375-409, April.
    2. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    3. Jing Sun, 2016. "Composite quantile regression for single-index models with asymmetric errors," Computational Statistics, Springer, vol. 31(1), pages 329-351, March.
    4. Poeschel, Friedrich, 2012. "Assortative matching through signals," IAB-Discussion Paper 201215, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    5. Yu, Lining & Härdle, Wolfgang Karl & Borke, Lukas & Benschop, Thijs, 2017. "FRM: A financial risk meter based on penalizing tail events occurrence," SFB 649 Discussion Papers 2017-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Lining Yu & Wolfgang Karl Hardle & Lukas Borke & Thijs Benschop, 2020. "An AI approach to measuring financial risk," Papers 2009.13222, arXiv.org.

  9. Guo, Xu & Wong, Wing-Keung & Zhu, Lixing, 2013. "An analysis of portfolio selection with multiplicative background risk," MPRA Paper 51331, University Library of Munich, Germany.

    Cited by:

    1. Xu, Guo & Wing-Keung, Wong & Lixing, Zhu, 2013. "Comparisons and Characterizations of the Mean-Variance, Mean-VaR, Mean-CVaR Models for Portfolio Selection With Background Risk," MPRA Paper 51827, University Library of Munich, Germany.

  10. Lin, Lu & Li, Feng & Zhu, Lixing & Härdle, Wolfgang Karl, 2010. "Mean volatility regressions," SFB 649 Discussion Papers 2011-003, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Anand, Kartik & Gai, Prasanna & Marsili, Matteo, 2011. "Rollover risk, network structure and systemic financial crises," SFB 649 Discussion Papers 2011-052, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    2. Scheffel, Juliane, 2011. "Compensation of unusual working schedules," SFB 649 Discussion Papers 2011-026, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    3. Mechtenberg, Lydia & Münster, Johannes, 2011. "A strategic mediator who is biased into the same direction as the expert can improve information transmission," SFB 649 Discussion Papers 2011-012, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    4. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang Karl, 2011. "TVICA - time varying independent component analysis and its application to financial data," SFB 649 Discussion Papers 2011-054, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    5. Stahlschmidt, Stephan & Tausendteufel, Helmut & Härdle, Wolfgang Karl, 2011. "Bayesian Networks and sex-related homicides," SFB 649 Discussion Papers 2011-045, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    6. Bocart, Fabian Y.R.P. & Hafner, Christian M., 2012. "Econometric analysis of volatile art markets," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3091-3104.
    7. Mammen, Enno & Rothe, Christoph & Schienle, Melanie, 2014. "Semiparametric Estimation with Generated Covariates," SFB 649 Discussion Papers 2014-043, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    8. Hautsch, Nikolaus & Schaumburg, Julia & Schienle, Melanie, 2011. "Financial network systemic risk contributions," SFB 649 Discussion Papers 2011-072, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    9. Kappus, Johanna & Reiß, Markus, 2010. "Estimation of the characteristics of a Lévy process observed at arbitrary frequency," SFB 649 Discussion Papers 2010-015, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    10. Bertrand, Aurelie & Hafner, Christian, 2011. "On heterogeneous latent class models with applications to the analysis of rating scores," LIDAM Discussion Papers ISBA 2011028, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Hautsch, Nikolaus & Huang, Ruihong, 2011. "Limit order flow, market impact and optimal order sizes: Evidence from NASDAQ TotalView-ITCH data," SFB 649 Discussion Papers 2011-056, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    12. Fiocco, Raffaele & Scarpa, Carlo, 2011. "The regulation of interdependent markets," SFB 649 Discussion Papers 2011-046, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    13. BAUWENS, Luc & HAFNER, Christian M. & PIERRET, Diane, 2013. "Multivariate volatility modeling of electricity futures," LIDAM Reprints CORE 2526, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. Horst, Ulrich & Kupper, Michael & Macrina, Andrea & Mainberger, Christoph, 2011. "Continuous equilibrium under base preferences and attainable initial endowments," SFB 649 Discussion Papers 2011-082, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    15. Heyne, Gregor & Kupper, Michael & Mainberger, Christoph, 2011. "Minimal supersolutions of BSDEs with lower semicontinuous generations," SFB 649 Discussion Papers 2011-067, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    16. Santiago Moreno-Bromberg & Luca Taschini, 2011. "Pollution permits, Strategic Trading and Dynamic Technology Adoption," Papers 1103.2914, arXiv.org.
    17. Härdle, Wolfgang Karl & Osipenko, Maria, 2011. "Pricing Chinese rain: A multisite mulit-period equilibrium pricing model for rainfall derivatives," SFB 649 Discussion Papers 2011-055, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. Bindseil, Ulrich & König, Philipp Johann, 2011. "The economics of TARGET2 balances," SFB 649 Discussion Papers 2011-035, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Cebiroğlu, Gökhan & Horst, Ulrich, 2011. "Optimal display of Iceberg orders," SFB 649 Discussion Papers 2011-057, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    20. Tischer, Sven & Hildebrandt, Lutz, 2011. "Linking corporate reputation and shareholder value using the publication of reputation rankings," SFB 649 Discussion Papers 2011-065, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    21. Hofmann, Dirk & Qari, Salmai, 2011. "The law of attraction bilateral search and horizontal heterogeneity," SFB 649 Discussion Papers 2011-017, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    22. Schneider, Dorothee, 2011. "The labor share: A review of theory and evidence," SFB 649 Discussion Papers 2011-069, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    23. Raffaele Fiocco & Mario Gilli, 2011. "Bargaining and Collusion in a Regulatory Model," Working Papers 207, University of Milano-Bicocca, Department of Economics, revised Mar 2011.
    24. Reiß, Markus & Rozenholc, Yves & Cuenod, Charles A., 2011. "Pointwise adaptive estimation for quantile regression," SFB 649 Discussion Papers 2011-029, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    25. Naujokat, Felix & Horst, Ulrich, 2011. "When to cross the spread: Curve following with singular control," SFB 649 Discussion Papers 2011-053, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    26. Gentle, James E. & Härdle, Wolfgang Karl & Mori, Yuichi, 2011. "How computational statistics became the backbone of modern data science," SFB 649 Discussion Papers 2011-020, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    27. Cheridito, Patrick & Horst, Ulrich & Kupper, Michael & Pirvu, Traian A., 2011. "Equilibrium pricing in incomplete markets under translation invariant preferences," SFB 649 Discussion Papers 2011-083, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    28. Myšičková, Alena & Song, Song & Majer, Piotr & Mohr, Peter N. C. & Heekeren, Hauke R. & Härdle, Wolfgang Karl, 2011. "Risk patterns and correlated brain activities: Multidimensional statistical analysis of fMRI data with application to risk patterns," SFB 649 Discussion Papers 2011-085, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    29. Fiocco, Raffaele, 2011. "Competition and regulation in a differentiated good market," SFB 649 Discussion Papers 2011-084, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    30. Kratz, Peter & Schöneborn, Torsten, 2011. "Optimal liquidation in dark pools," SFB 649 Discussion Papers 2011-058, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    31. Bibinger, Markus, 2011. "Asymptotics of asynchronicity," SFB 649 Discussion Papers 2011-033, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    32. Bibinger, Markus, 2011. "An estimator for the quadratic covariation of asynchronously observed Itô processes with noise: Asymptotic distribution theory," SFB 649 Discussion Papers 2011-034, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    33. Meyer-Gohde, Alexander, 2011. "Monetary policy, determinacy, and the natural rate hypothesis," SFB 649 Discussion Papers 2011-049, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    34. Moreno-Bromberg, Santiago & Pirvu, Traian A. & Réveillac, Anthony, 2011. "CRRA utility maximization under risk constraints," SFB 649 Discussion Papers 2011-043, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

  11. Cui, Xia & Härdle, Wolfgang Karl & Zhu, Lixing, 2009. "Generalized single-index models: The EFM approach," SFB 649 Discussion Papers 2009-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Gerhard Tutz & Sebastian Petry, 2016. "Generalized additive models with unknown link function including variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2866-2885, November.

  12. Cui, Xia & Härdle, Wolfgang Karl & Zhu, Lixing, 2009. "Generalized single-index models: The EFM approach," SFB 649 Discussion Papers 2009-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. Gerhard Tutz & Sebastian Petry, 2016. "Generalized additive models with unknown link function including variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(15), pages 2866-2885, November.

Articles

  1. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

    Cited by:

    1. Hayley Randall & Andreas Artemiou & Xingye Qiao, 2021. "Sufficient dimension reduction based on distance‐weighted discrimination," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(4), pages 1186-1211, December.
    2. Zhang, Fode & Ng, Hon Keung Tony & Shi, Yimin, 2020. "Mis-specification analysis of Wiener degradation models by using f-divergence with outliers," Reliability Engineering and System Safety, Elsevier, vol. 195(C).

  2. Iaci, Ross & Yin, Xiangrong & Zhu, Lixing, 2016. "The Dual Central Subspaces in dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 178-189.

    Cited by:

    1. Alothman, Ahmad & Dong, Yuexiao & Artemiou, Andreas, 2018. "On dual model-free variable selection with two groups of variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 366-377.

  3. Lin, Lu & Zhu, Lixing & Gai, Yujie, 2016. "Inference for biased models: A quasi-instrumental variable approach," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 22-36.

    Cited by:

    1. Lu, Jun & Zhu, Xuehu & Lin, Lu & Zhu, Lixing, 2019. "Estimation for biased partial linear single index models," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 1-13.
    2. Zhu, Xuehu & Wang, Tao & Zhao, Junlong & Zhu, Lixing, 2017. "Inference for biased transformation models," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 105-120.

  4. Xuehu Zhu & Xu Guo & Lu Lin & Lixing Zhu, 2016. "Testing for positive expectation dependence," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(1), pages 135-153, February.

    Cited by:

    1. Li, Jingyuan & Liu, Dongri & Wang, Jianli, 2016. "Risk aversion with two risks: A theoretical extension," Journal of Mathematical Economics, Elsevier, vol. 63(C), pages 100-105.
    2. Guo, Xu & Li, Jingyuan, 2016. "Confidence band for expectation dependence with applications," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 141-149.
    3. Georges Dionne & Jingyuan Li & Cédric Okou, 2024. "An alternative representation of the C-CAPM with higher-order risks," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 49(2), pages 194-233, September.
    4. Denuit, Michel & Trufin, Julien & Verdebout, Thomas, 2021. "Testing for more positive expectation dependence with application to model comparison," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 163-172.
    5. Denuit, Michel & Huyghe, Julie & Trufin, Julien & Verdebout, Thomas, 2024. "Testing for auto-calibration with Lorenz and Concentration curves," Insurance: Mathematics and Economics, Elsevier, vol. 117(C), pages 130-139.

  5. Wangli Xu & Lixing Zhu, 2015. "Nonparametric check for partial linear errors-in-covariables models with validation data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 793-815, August.

    Cited by:

    1. Zhihua Sun & Dongshan Luo & Xiaohua Zhou & Qingzhao Zhang, 2021. "Comparative studies on the adequacy check of parametric measurement error models with auxiliary variable," Statistical Papers, Springer, vol. 62(4), pages 1723-1751, August.
    2. Otsu, Taisuke & Taylor, Luke, 2020. "Specification testing for errors-in-variables models," LSE Research Online Documents on Economics 102690, London School of Economics and Political Science, LSE Library.
    3. Sun, Zhihua & Chen, Feifei & Zhou, Xiaohua & Zhang, Qingzhao, 2017. "Improved model checking methods for parametric models with responses missing at random," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 147-161.

  6. Chen, Fei & Li, Zaixing & Shi, Lei & Zhu, Lixing, 2015. "Inference for mixed models of ANOVA type with high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 382-401.

    Cited by:

    1. Simona Buscemi & Antonella Plaia, 2020. "Model selection in linear mixed-effect models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 529-575, December.

  7. Zhou, Jingke & Xu, Wangli & Zhu, Lixing, 2015. "Robust estimating equation-based sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 99-118.

    Cited by:

    1. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

  8. Zhu, Xuehu & Guo, Xu & Lin, Lu & Zhu, Lixing, 2015. "Heteroscedasticity checks for single index models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 41-55.

    Cited by:

    1. Zhu, Xuehu & Chen, Fei & Guo, Xu & Zhu, Lixing, 2016. "Heteroscedasticity testing for regression models: A dimension reduction-based model adaptive approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 263-283.

  9. Xu Guo & Wangli Xu & Lixing Zhu, 2015. "Model checking for parametric regressions with response missing at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(2), pages 229-259, April.

    Cited by:

    1. Cui, Li-E & Zhao, Puying & Tang, Niansheng, 2022. "Generalized empirical likelihood for nonsmooth estimating equations with missing data," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Guo, Xu & Song, Lianlian & Fang, Yun & Zhu, Lixing, 2019. "Model checking for general linear regression with nonignorable missing response," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 1-12.
    3. Sun, Zhihua & Chen, Feifei & Zhou, Xiaohua & Zhang, Qingzhao, 2017. "Improved model checking methods for parametric models with responses missing at random," Journal of Multivariate Analysis, Elsevier, vol. 154(C), pages 147-161.

  10. Long Feng & Changliang Zou & Zhaojun Wang & Lixing Zhu, 2015. "Robust comparison of regression curves," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 185-204, March.

    Cited by:

    1. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    2. Kathrin Möllenhoff & Frank Bretz & Holger Dette, 2020. "Equivalence of regression curves sharing common parameters," Biometrics, The International Biometric Society, vol. 76(2), pages 518-529, June.
    3. Boente, Graciela & Pardo-Fernández, Juan Carlos, 2016. "Robust testing for superiority between two regression curves," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 151-168.
    4. Cuizhen Niu & Lixing Zhu, 2018. "A robust adaptive-to-model enhancement test for parametric single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1013-1045, October.

  11. Zhu, L. & Li, M.S. & Wu, Q.H. & Jiang, L., 2015. "Short-term natural gas demand prediction based on support vector regression with false neighbours filtered," Energy, Elsevier, vol. 80(C), pages 428-436.

    Cited by:

    1. Yukseltan, Ergun & Yucekaya, Ahmet & Bilge, Ayse Humeyra & Agca Aktunc, Esra, 2021. "Forecasting models for daily natural gas consumption considering periodic variations and demand segregation," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
    2. Song, Jiancai & Zhang, Liyi & Jiang, Qingling & Ma, Yunpeng & Zhang, Xinxin & Xue, Guixiang & Shen, Xingliang & Wu, Xiangdong, 2022. "Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model," Applied Energy, Elsevier, vol. 309(C).
    3. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    4. Konstantinos Papageorgiou & Elpiniki I. Papageorgiou & Katarzyna Poczeta & Dionysis Bochtis & George Stamoulis, 2020. "Forecasting of Day-Ahead Natural Gas Consumption Demand in Greece Using Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 13(9), pages 1-32, May.
    5. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    6. Marta P. Fernandes & Joaquim L. Viegas & Susana M. Vieira & João M. C. Sousa, 2017. "Segmentation of Residential Gas Consumers Using Clustering Analysis," Energies, MDPI, vol. 10(12), pages 1-26, December.
    7. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    8. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
    9. Noorollahi, Younes & Golshanfard, Aminabbas & Ansaripour, Shiva & Khaledi, Arian & Shadi, Mehdi, 2021. "Solar energy for sustainable heating and cooling energy system planning in arid climates," Energy, Elsevier, vol. 218(C).
    10. Ravnik, J. & Hriberšek, M., 2019. "A method for natural gas forecasting and preliminary allocation based on unique standard natural gas consumption profiles," Energy, Elsevier, vol. 180(C), pages 149-162.
    11. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
    12. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    13. Zha, Wenshu & Liu, Yuping & Wan, Yujin & Luo, Ruilan & Li, Daolun & Yang, Shan & Xu, Yanmei, 2022. "Forecasting monthly gas field production based on the CNN-LSTM model," Energy, Elsevier, vol. 260(C).
    14. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    15. Emmanuel Flavian Sapnken & Jean Gaston Tamba & Salome Njakomo Essiane & Francis Djanna Koffi & Donatien Njomo, 2018. "Modeling and Forecasting Gasoline Consumption in Cameroon using Linear Regression Models," International Journal of Energy Economics and Policy, Econjournals, vol. 8(2), pages 111-120.
    16. Huanying Liu & Yulin Liu & Changhao Wang & Yanling Song & Wei Jiang & Cuicui Li & Shouxin Zhang & Bingyuan Hong, 2023. "Natural Gas Demand Forecasting Model Based on LASSO and Polynomial Models and Its Application: A Case Study of China," Energies, MDPI, vol. 16(11), pages 1-15, May.
    17. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
    18. Chansu Lim, 2019. "Estimating Residential and Industrial City Gas Demand Function in the Republic of Korea—A Kalman Filter Application," Sustainability, MDPI, vol. 11(5), pages 1-12, March.
    19. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    20. Du, Jian & Zheng, Jianqin & Liang, Yongtu & Lu, Xinyi & Klemeš, Jiří Jaromír & Varbanov, Petar Sabev & Shahzad, Khurram & Rashid, Muhammad Imtiaz & Ali, Arshid Mahmood & Liao, Qi & Wang, Bohong, 2022. "A hybrid deep learning framework for predicting daily natural gas consumption," Energy, Elsevier, vol. 257(C).
    21. Deng, Yanqiao & Ma, Xin & Zhang, Peng & Cai, Yubin, 2022. "Multi-step ahead forecasting of daily urban gas load in Chengdu using a Tanimoto kernel-based NAR model and Whale optimization," Energy, Elsevier, vol. 260(C).
    22. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    23. Hafezi, Reza & Akhavan, AmirNaser & Pakseresht, Saeed & A. Wood, David, 2021. "Global natural gas demand to 2025: A learning scenario development model," Energy, Elsevier, vol. 224(C).
    24. Wei, Nan & Yin, Lihua & Li, Chao & Li, Changjun & Chan, Christine & Zeng, Fanhua, 2021. "Forecasting the daily natural gas consumption with an accurate white-box model," Energy, Elsevier, vol. 232(C).
    25. Sen, Doruk & Tunç, K.M. Murat & Günay, M. Erdem, 2021. "Forecasting electricity consumption of OECD countries: A global machine learning modeling approach," Utilities Policy, Elsevier, vol. 70(C).
    26. Reza Hafezi & Amir Naser Akhavan & Mazdak Zamani & Saeed Pakseresht & Shahaboddin Shamshirband, 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand," Energies, MDPI, vol. 12(21), pages 1-22, October.
    27. Mustafa Akpinar & Nejat Yumusak, 2016. "Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods," Energies, MDPI, vol. 9(9), pages 1-17, September.
    28. Guo-Feng Fan & An Wang & Wei-Chiang Hong, 2018. "Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting," Energies, MDPI, vol. 11(7), pages 1-21, June.
    29. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    30. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    31. Nan Wei & Changjun Li & Jiehao Duan & Jinyuan Liu & Fanhua Zeng, 2019. "Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model," Energies, MDPI, vol. 12(2), pages 1-15, January.
    32. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
    33. Garg, A. & Lam, Jasmine Siu Lee, 2017. "Design of explicit models for estimating efficiency characteristics of microbial fuel cells," Energy, Elsevier, vol. 134(C), pages 136-156.
    34. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
    35. Askari, S. & Montazerin, N. & Fazel Zarandi, M.H., 2016. "Gas networks simulation from disaggregation of low frequency nodal gas consumption," Energy, Elsevier, vol. 112(C), pages 1286-1298.
    36. Lin, Zijie & Xie, Linbo & Zhang, Siyuan, 2024. "A compound framework for short-term gas load forecasting combining time-enhanced perception transformer and two-stage feature extraction," Energy, Elsevier, vol. 298(C).
    37. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    38. Ergun Yukseltan & Ahmet Yucekaya & Ayse Humeyra Bilge & Esra Agca Aktunc, 2020. "Forecasting Models for Daily Natural Gas Consumption Considering Periodic Variations and Demand Segregation," Papers 2003.13385, arXiv.org.
    39. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    40. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    41. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.

  12. Peirong Xu & Ji Zhu & Lixing Zhu & Yi Li, 2015. "Covariance-enhanced discriminant analysis," Biometrika, Biometrika Trust, vol. 102(1), pages 33-45.

    Cited by:

    1. Sheng, Ying & Wang, Qihua, 2019. "Simultaneous variable selection and class fusion with penalized distance criterion based classifiers," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 138-152.
    2. Aaron J Molstad & Adam J Rothman, 2018. "Shrinking characteristics of precision matrix estimators," Biometrika, Biometrika Trust, vol. 105(3), pages 563-574.
    3. Kang, Xiaoning & Kang, Lulu & Chen, Wei & Deng, Xinwei, 2022. "A generative approach to modeling data with quantitative and qualitative responses," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    4. Briggs, Kristie, 2015. "Co-owner relationships conducive to high quality joint patents," Research Policy, Elsevier, vol. 44(8), pages 1566-1573.
    5. Pan, Yuqing & Mai, Qing, 2020. "Efficient computation for differential network analysis with applications to quadratic discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    6. Luo, Shan & Chen, Zehua, 2020. "A procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification," Journal of Multivariate Analysis, Elsevier, vol. 179(C).

  13. S. Du & F. Ma & Z. Fu & L. Zhu & J. Zhang, 2015. "Game-theoretic analysis for an emission-dependent supply chain in a ‘cap-and-trade’ system," Annals of Operations Research, Springer, vol. 228(1), pages 135-149, May.

    Cited by:

    1. Song, Shuang & Govindan, Kannan & Xu, Lei & Du, Peng & Qiao, Xiaojiao, 2017. "Capacity and production planning with carbon emission constraints," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 132-150.
    2. Sina Abbasi & Babek Erdebilli, 2023. "Green Closed-Loop Supply Chain Networks’ Response to Various Carbon Policies during COVID-19," Sustainability, MDPI, vol. 15(4), pages 1-30, February.
    3. Chen, Yuyu & Li, Bangyi & Zhang, Guoqing & Bai, Qingguo, 2020. "Quantity and collection decisions of the remanufacturing enterprise under both the take-back and carbon emission capacity regulations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    4. Chunhai Yu & Yingxiang Zhang & Ling Liu & Thomas W. Archibald, 2024. "Low-carbon supply chain strategy and contract coordination considering manufacturers′ fairness concerns," Operational Research, Springer, vol. 24(4), pages 1-51, December.
    5. Zhitao Xu & Adel Elomri & Shaligram Pokharel & Fatih Mutlu, 2019. "The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models," Sustainability, MDPI, vol. 11(11), pages 1-20, May.
    6. Linda Zhang & Gang D.U. & Jun W.U. & Yujie M.A., 2020. "Joint production planning, pricing and retailer selection with emission control based on Stackelberg game and nested genetic algorithm," Post-Print hal-03276837, HAL.
    7. Chun-Hung Chiu & Gang Hao & Xin Dai & Hang Xie, 2020. "Inventory sharing of professional optics product supply chain with equal power agents," Annals of Operations Research, Springer, vol. 291(1), pages 169-194, August.
    8. Liang Shen & Xiaodi Wang & Qinqin Liu & Yuyan Wang & Lingxue Lv & Rongyun Tang, 2021. "Carbon Trading Mechanism, Low-Carbon E-Commerce Supply Chain and Sustainable Development," Mathematics, MDPI, vol. 9(15), pages 1-26, July.
    9. Juanjuan Qin & Liguo Ren & Liangjie Xia, 2017. "Carbon Emission Reduction and Pricing Strategies of Supply Chain under Various Demand Forecasting Scenarios," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(01), pages 1-27, February.
    10. Tong Shu & Qian Liu & Shou Chen & Shouyang Wang & Kin Keung Lai, 2018. "Pricing Decisions of CSR Closed-Loop Supply Chains with Carbon Emission Constraints," Sustainability, MDPI, vol. 10(12), pages 1-25, November.
    11. Wang, Xi & Cai, Hua & Florig, H. Keith, 2016. "Energy-saving implications from supply chain improvement: An exploratory study on China's consumer goods retail system," Energy Policy, Elsevier, vol. 95(C), pages 411-420.
    12. Yi Zheng & Huchang Liao & Xue Yang, 2016. "Stochastic Pricing and Order Model with Transportation Mode Selection for Low-Carbon Retailers," Sustainability, MDPI, vol. 8(1), pages 1-19, January.
    13. Bai, Qingguo & Chen, Mingyuan & Xu, Lei, 2017. "Revenue and promotional cost-sharing contract versus two-part tariff contract in coordinating sustainable supply chain systems with deteriorating items," International Journal of Production Economics, Elsevier, vol. 187(C), pages 85-101.
    14. Smita Rani & Rashid Ali & Anchal Agarwal, 2019. "Fuzzy inventory model for deteriorating items in a green supply chain with carbon concerned demand," OPSEARCH, Springer;Operational Research Society of India, vol. 56(1), pages 91-122, March.
    15. Wen-Hsien Tsai, 2018. "Carbon Taxes and Carbon Right Costs Analysis for the Tire Industry," Energies, MDPI, vol. 11(8), pages 1-22, August.
    16. Yao, Liming & He, Linhuan & Chen, Xudong & Yang, Ling, 2021. "A study on the profit distribution mechanism of the resource - Based supply chain considering low-carbon constraints and ecological restoration," Resources Policy, Elsevier, vol. 74(C).
    17. Longfei He & Baiyun Yuan & Junsong Bian & Kin Keung Lai, 2023. "Differential game theoretic analysis of the dynamic emission abatement in low-carbon supply chains," Annals of Operations Research, Springer, vol. 324(1), pages 355-393, May.
    18. Shaofu Du & Yujiao Zhu & Yangguang Zhu & Wenzhi Tang, 2020. "Allocation policy considering firm’s time-varying emission reduction in a cap-and-trade system," Annals of Operations Research, Springer, vol. 290(1), pages 543-565, July.
    19. K. T. Shibin & Rameshwar Dubey & Angappa Gunasekaran & Benjamin Hazen & David Roubaud & Shivam Gupta & Cyril Foropon, 2020. "Examining sustainable supply chain management of SMEs using resource based view and institutional theory," Annals of Operations Research, Springer, vol. 290(1), pages 301-326, July.
    20. Yang, Huixiao & Luo, Jianwen & Wang, Haijun, 2017. "The role of revenue sharing and first-mover advantage in emission abatement with carbon tax and consumer environmental awareness," International Journal of Production Economics, Elsevier, vol. 193(C), pages 691-702.
    21. Xu, Song & Govindan, Kannan & Wang, Wanru & Yang, Wenting, 2024. "Supply chain management under cap-and-trade regulation: A literature review and research opportunities," International Journal of Production Economics, Elsevier, vol. 271(C).
    22. Wen Tong & Jianbang Du & Fu Zhao & Dong Mu & John W. Sutherland, 2019. "Optimal Joint Production and Emissions Reduction Strategies Considering Consumers’ Environmental Preferences: A Manufacturer’s Perspective," Sustainability, MDPI, vol. 11(2), pages 1-15, January.
    23. Bibhas C. Giri & Ishani Ray, 2022. "Optimal sustainability investment and pricing decisions in a two-echelon supply chain with emissions-sensitive demand under cap-and-trade policy," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 786-808, September.
    24. Chen, Xu & Wang, Xiaojun & Chan, Hing Kai, 2017. "Manufacturer and retailer coordination for environmental and economic competitiveness: A power perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 268-281.
    25. Zhang, Suyong & Wang, Chuanxu & Yu, Chao, 2019. "The evolutionary game analysis and simulation with system dynamics of manufacturer's emissions abatement behavior under cap-and-trade regulation," Applied Mathematics and Computation, Elsevier, vol. 355(C), pages 343-355.
    26. Yonghong Cheng & Zhongkai Xiong & Qinglin Luo, 2018. "Joint Pricing and Product Carbon Footprint Decisions and Coordination of Supply Chain with Cap-and-Trade Regulation," Sustainability, MDPI, vol. 10(2), pages 1-24, February.
    27. SungYong Choi & KyungBae Park & Sang-Oh Shim, 2019. "The Optimal Emission Decisions of Sustainable Production with Innovative Baseline Credit Regulations," Sustainability, MDPI, vol. 11(6), pages 1-16, March.
    28. Peng Wu & Yixi Yin & Shiying Li & Yulong Huang, 2018. "Low-Carbon Supply Chain Management Considering Free Emission Allowance and Abatement Cost Sharing," Sustainability, MDPI, vol. 10(7), pages 1-18, June.
    29. Chenhao Fang & Tieju Ma, 2021. "Technology adoption with carbon emission trading mechanism: modeling with heterogeneous agents and uncertain carbon price," Annals of Operations Research, Springer, vol. 300(2), pages 577-600, May.
    30. Jiang, Jingjing & Xie, Dejun & Ye, Bin & Shen, Bo & Chen, Zhanming, 2016. "Research on China’s cap-and-trade carbon emission trading scheme: Overview and outlook," Applied Energy, Elsevier, vol. 178(C), pages 902-917.
    31. Jinpyo Lee & Mi Lim Lee & Minjae Park, 2018. "A Newsboy Model with Quick Response under Sustainable Carbon Cap-N-Trade," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
    32. Zhang, Hongyu & Zhang, Da & Zhang, Xiliang, 2023. "The role of output-based emission trading system in the decarbonization of China's power sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    33. Hao Zou & Jin Qin & Bo Dai, 2021. "Optimal Pricing Decisions for a Low-Carbon Supply Chain Considering Fairness Concern under Carbon Quota Policy," IJERPH, MDPI, vol. 18(2), pages 1-21, January.
    34. Sungyong Choi, 2018. "A Loss-Averse Newsvendor with Cap-and-Trade Carbon Emissions Regulation," Sustainability, MDPI, vol. 10(7), pages 1-12, June.
    35. Meng, Xiaoge & Yao, Zhong & Nie, Jiajia & Zhao, Yingxue & Li, Zenglu, 2018. "Low-carbon product selection with carbon tax and competition: Effects of the power structure," International Journal of Production Economics, Elsevier, vol. 200(C), pages 224-230.
    36. Saeid Rezaei & Reza Maihami, 2020. "Optimizing the sustainable decisions in a multi-echelon closed-loop supply chain of the manufacturing/remanufacturing products with a competitive environment," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(7), pages 6445-6471, October.
    37. Weihao Wang & Deqing Ma & Jinsong Hu, 2023. "Study of Carbon Reduction and Marketing Decisions with the Envisioning of a Favorable Event under Cap-and-Trade Regulation," IJERPH, MDPI, vol. 20(5), pages 1-27, March.
    38. Longfei He & Chenglin Hu & Daozhi Zhao & Haili Lu & Xiaoxi Fu & Yiyu Li, 2016. "Carbon emission mitigation through regulatory policies and operations adaptation in supply chains: theoretic developments and extensions," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(1), pages 179-207, November.
    39. Yongjian Li & Qianzhou Deng & Chi Zhou & Lipan Feng, 2020. "Environmental governance strategies in a two-echelon supply chain with tax and subsidy interactions," Annals of Operations Research, Springer, vol. 290(1), pages 439-462, July.
    40. Huang, Weixiang & Zhou, Wenhui & Chen, Jiguang & Chen, Xiaohong, 2019. "The government’s optimal subsidy scheme under Manufacturers’ competition of price and product energy efficiency," Omega, Elsevier, vol. 84(C), pages 70-101.
    41. Weihao Wang & Deqing Ma & Jinsong Hu, 2022. "Dynamic Carbon Reduction and Marketing Strategies with Consumers’ Environmental Awareness under Cap-and-Trade Regulation," Sustainability, MDPI, vol. 14(16), pages 1-31, August.
    42. Avi Herbon & Matan Shnaiderman & Tatyana Chernonog, 2018. "Postponed two-pricing and ordering opportunity for selling a single season inventoried product," Annals of Operations Research, Springer, vol. 271(2), pages 619-640, December.
    43. Hong, Zhaofu & Chu, Chengbin & Zhang, Linda L. & Yu, Yugang, 2017. "Optimizing an emission trading scheme for local governments: A Stackelberg game model and hybrid algorithm," International Journal of Production Economics, Elsevier, vol. 193(C), pages 172-182.
    44. Kuiti, Mithu Rani & Ghosh, Debabrata & Basu, Preetam & Bisi, Arnab, 2020. "Do cap-and-trade policies drive environmental and social goals in supply chains: Strategic decisions, collaboration, and contract choices," International Journal of Production Economics, Elsevier, vol. 223(C).
    45. Xiaogang Ma & Chunyu Bao & Jizi Li & Wandong Lou, 2022. "The impact of dual fairness concerns on bargaining game and its dynamic system stability," Annals of Operations Research, Springer, vol. 318(1), pages 357-382, November.
    46. Chen, Xu & Wang, Xiaojun & Zhou, Mingmei, 2019. "Firms’ green R&D cooperation behaviour in a supply chain: Technological spillover, power and coordination," International Journal of Production Economics, Elsevier, vol. 218(C), pages 118-134.
    47. Xu, Song & Fang, Lei, 2020. "Partial credit guarantee and trade credit in an emission-dependent supply chain with capital constraint," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    48. Ma, Xueli & Wang, Jian & Bai, Qingguo & Wang, Shuyun, 2020. "Optimization of a three-echelon cold chain considering freshness-keeping efforts under cap-and-trade regulation in Industry 4.0," International Journal of Production Economics, Elsevier, vol. 220(C).
    49. Yuan, Jiahang & Wang, Li & Li, Yun, 2024. "Carbon emission reduction dynamic decision-making in the electricity supply chain with different carbon emission allowance principles," Energy, Elsevier, vol. 312(C).
    50. Shaofu Du & Jun Qian & Tianzhuo Liu & Li Hu, 2020. "Emission allowance allocation mechanism design: a low-carbon operations perspective," Annals of Operations Research, Springer, vol. 291(1), pages 247-280, August.
    51. Shuyi Wang & Zhenhua Wu & Baochen Yang, 2018. "Decision and Performance Analysis of a Price-Setting Manufacturer with Options under a Flexible-Cap Emission Trading Scheme (ETS)," Sustainability, MDPI, vol. 10(10), pages 1-22, October.
    52. Rui, Zhaobiao & Peng, Weicai & Qin, Ximei & Wang, Jun, 2023. "Assessing carbon cap-and-trade policies on hybrid renewable energy investments: Implications for pricing and capacity decisions," Resources Policy, Elsevier, vol. 86(PA).
    53. Xiaoyan Wang & Minggao Xue & Lu Xing, 2018. "Analysis of Carbon Emission Reduction in a Dual-Channel Supply Chain with Cap-And-Trade Regulation and Low-Carbon Preference," Sustainability, MDPI, vol. 10(3), pages 1-18, February.
    54. Qiang Du & Jiajie Zhou, 2022. "Evolution of Low Carbon Supply Chain Research: A Systematic Bibliometric Analysis," IJERPH, MDPI, vol. 19(23), pages 1-20, November.
    55. E. Allevi & A. Gnudi & I. V. Konnov & G. Oggioni, 2018. "Decomposition method for oligopolistic competitive models with common environmental regulation," Annals of Operations Research, Springer, vol. 268(1), pages 441-467, September.
    56. Zhi Liu & Xiao-Xue Zheng & Ben-Gang Gong & Yun-Miao Gui, 2017. "Joint Decision-Making and the Coordination of a Sustainable Supply Chain in the Context of Carbon Tax Regulation and Fairness Concerns," IJERPH, MDPI, vol. 14(12), pages 1-20, November.
    57. Qingguo Bai & Jianteng Xu & Yuzhong Zhang, 2022. "The distributionally robust optimization model for a remanufacturing system under cap-and-trade policy: a newsvendor approach," Annals of Operations Research, Springer, vol. 309(2), pages 731-760, February.
    58. Liangjie Xia & Tingting Guo & Juanjuan Qin & Xiaohang Yue & Ning Zhu, 2018. "Carbon emission reduction and pricing policies of a supply chain considering reciprocal preferences in cap-and-trade system," Annals of Operations Research, Springer, vol. 268(1), pages 149-175, September.
    59. Panpan Li & Baojun Zhang & Ying Ji, 2024. "A study on supply chain optimization with uncertain demand under the cap-and-trade system," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17449-17473, July.
    60. Peiyue Cheng & Guitao Zhang & Hao Sun, 2022. "The Sustainable Supply Chain Network Competition Based on Non-Cooperative Equilibrium under Carbon Emission Permits," Mathematics, MDPI, vol. 10(9), pages 1-31, April.
    61. Fang, Yuan & Yu, Yugang & Shi, Ye & Liu, Jie, 2020. "The effect of carbon tariffs on global emission control: A global supply chain model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    62. Yufei Wang & Mangirdas Morkūnas & Jinzhao Wei, 2024. "Strategic Synergies: Unveiling the Interplay of Game Theory and Cultural Dynamics in a Globalized World," Games, MDPI, vol. 15(4), pages 1-25, June.
    63. Gongbing Bi & Minyue Jin & Liuyi Ling & Feng Yang, 2017. "Environmental subsidy and the choice of green technology in the presence of green consumers," Annals of Operations Research, Springer, vol. 255(1), pages 547-568, August.
    64. Guang Zhu & Gaozhi Pan & Weiwei Zhang, 2018. "Evolutionary Game Theoretic Analysis of Low Carbon Investment in Supply Chains under Governmental Subsidies," IJERPH, MDPI, vol. 15(11), pages 1-27, November.
    65. Wen Jiang & Wenfei Lu & Qianwen Xu, 2019. "Profit Distribution Model for Construction Supply Chain with Cap-and-Trade Policy," Sustainability, MDPI, vol. 11(4), pages 1-24, February.

  14. Guo, Xu & Post, Thierry & Wong, Wing-Keung & Zhu, Lixing, 2014. "Moment conditions for Almost Stochastic Dominance," Economics Letters, Elsevier, vol. 124(2), pages 163-167.
    See citations under working paper version above.
  15. T. Wang & X. Guo & L. Zhu & P. Xu, 2014. "Transformed sufficient dimension reduction," Biometrika, Biometrika Trust, vol. 101(4), pages 815-829.

    Cited by:

    1. Tao, Chenyang & Feng, Jianfeng, 2017. "Canonical kernel dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 107(C), pages 131-148.
    2. Dong, Yuexiao & Yang, Chaozheng & Yu, Zhou, 2016. "On permutation tests for predictor contribution in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 81-91.
    3. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

  16. Guo, Xu & Xu, Wangli & Zhu, Lixing, 2014. "Multi-index regression models with missing covariates at random," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 345-363.

    Cited by:

    1. Timothy Reese & Majid Mojirsheibani, 2017. "On the $$L_p$$ L p norms of kernel regression estimators for incomplete data with applications to classification," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 81-112, March.
    2. Eric Han & Majid Mojirsheibani, 2021. "On histogram-based regression and classification with incomplete data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 635-662, July.
    3. Huilan Liu & Hu Yang & Changgen Peng, 2019. "Weighted composite quantile regression for single index model with missing covariates at random," Computational Statistics, Springer, vol. 34(4), pages 1711-1740, December.

  17. Cuizhen Niu & Xu Guo & Wangli Xu & Lixing Zhu, 2014. "Testing equality of shape parameters in several inverse Gaussian populations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(6), pages 795-809, August.

    Cited by:

    1. Amitava Mukherjee & Marco Marozzi, 2019. "A class of percentile modified Lepage-type tests," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(6), pages 657-689, August.
    2. Samadrita Bera & Nabakumar Jana, 2022. "On estimating common mean of several inverse Gaussian distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 115-139, January.
    3. Mohammad Reza Kazemi & Ali Akbar Jafari, 2019. "Inference about the shape parameters of several inverse Gaussian distributions: testing equality and confidence interval for a common value," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(5), pages 529-545, July.

  18. Peirong Xu & Lixing Zhu & Yi Li, 2014. "Ultrahigh dimensional time course feature selection," Biometrics, The International Biometric Society, vol. 70(2), pages 356-365, June.

    Cited by:

    1. Lv, Jing & Guo, Chaohui & Yang, Hu & Li, Yalian, 2017. "A moving average Cholesky factor model in covariance modeling for composite quantile regression with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 129-144.
    2. Zhang, Shen & Zhao, Peixin & Li, Gaorong & Xu, Wangli, 2019. "Nonparametric independence screening for ultra-high dimensional generalized varying coefficient models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 37-52.
    3. Tang, Niansheng & Xia, Linli & Yan, Xiaodong, 2019. "Feature screening in ultrahigh-dimensional partially linear models with missing responses at random," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 208-227.
    4. Qian, Junhui & Su, Liangjun, 2016. "Shrinkage estimation of common breaks in panel data models via adaptive group fused Lasso," Journal of Econometrics, Elsevier, vol. 191(1), pages 86-109.

  19. Feng, Zhenghui & Wang, Tao & Zhu, Lixing, 2014. "Transformation-based estimation," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 186-205.

    Cited by:

    1. Weichao Yang & Xu Guo & Niwen Zhou & Changliang Zou, 2024. "Connections between two classes of estimators for single‐index models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(3), pages 485-490, August.
    2. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    3. Jun Zhang & Junpeng Zhu & Zhenghui Feng, 2019. "Estimation and hypothesis test for single-index multiplicative models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 242-268, March.
    4. Zhu, Xuehu & Wang, Tao & Zhao, Junlong & Zhu, Lixing, 2017. "Inference for biased transformation models," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 105-120.
    5. Chen, Fei & Shi, Lei & Zhu, Xuehu & Zhu, Lixing, 2018. "Generalized principal Hessian directions for mixture multivariate skew elliptical distributions," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 142-159.
    6. Tan, Xin Lu, 2019. "Optimal estimation of slope vector in high-dimensional linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 179-204.
    7. Xie, Chuanlong & Zhu, Lixing, 2020. "Generalized kernel-based inverse regression methods for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

  20. Guo, Xu & Wang, Tao & Xu, Wangli & Zhu, Lixing, 2014. "Dimension reduction with missing response at random," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 228-242.

    Cited by:

    1. Deng, Jianqiu & Yang, Xiaojie & Wang, Qihua, 2022. "Surrogate space based dimension reduction for nonignorable nonresponse," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    2. Siming Zheng & Alan T. K. Wan & Yong Zhou, 2024. "Semiparametric recovery of central dimension reduction space with nonignorable nonresponse," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 374-396, May.
    3. Fan, Guo-Liang & Xu, Hong-Xia & Liang, Han-Ying, 2019. "Dimension reduction estimation for central mean subspace with missing multivariate response," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    4. Guo, Xu & Fang, Yun & Zhu, Xuehu & Xu, Wangli & Zhu, Lixing, 2018. "Semiparametric double robust and efficient estimation for mean functionals with response missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 325-339.
    5. Dong, Yuexiao & Xia, Qi & Tang, Cheng Yong & Li, Zeda, 2018. "On sufficient dimension reduction with missing responses through estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 126(C), pages 67-77.
    6. Wei Luo, 2022. "On efficient dimension reduction with respect to the interaction between two response variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 269-294, April.

  21. Xia Cui & Heng Peng & Songqiao Wen & Lixing Zhu, 2013. "Component Selection in the Additive Regression Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(3), pages 491-510, September.

    Cited by:

    1. Umberto Amato & Anestis Antoniadis & Italia De Feis, 2016. "Additive model selection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(4), pages 519-564, November.
    2. Zhenghui Feng & Lu Lin & Ruoqing Zhu & Lixing Zhu, 2020. "Nonparametric variable selection and its application to additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 827-854, June.
    3. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Arfan Raheen Afzal & Jing Yang & Xuewen Lu, 2021. "Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters," Computational Statistics, Springer, vol. 36(2), pages 829-855, June.

  22. Wang, Tao & Zhu, Lixing, 2013. "Sparse sufficient dimension reduction using optimal scoring," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 223-232.

    Cited by:

    1. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

  23. Wangli Xu & Lixing Zhu, 2013. "Testing the adequacy of varying coefficient models with missing responses at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 53-69, January.

    Cited by:

    1. Xu, Hong-Xia & Fan, Guo-Liang & Chen, Zhen-Long, 2017. "Hypothesis tests in partial linear errors-in-variables models with missing response," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 219-229.
    2. Yu-Ye Zou & Han-Ying Liang & Jing-Jing Zhang, 2015. "Nonlinear wavelet density estimation with data missing at random when covariates are present," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(8), pages 967-995, November.
    3. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.

  24. Zhou Yu & Liping Zhu & Heng Peng & Lixing Zhu, 2013. "Dimension reduction and predictor selection in semiparametric models," Biometrika, Biometrika Trust, vol. 100(3), pages 641-654.

    Cited by:

    1. Xiao, Zhen & Zhang, Qi, 2022. "Dimension reduction for block-missing data based on sparse sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    2. Girard, Stéphane & Lorenzo, Hadrien & Saracco, Jérôme, 2022. "Advanced topics in Sliced Inverse Regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    3. Weng, Jiaying, 2022. "Fourier transform sparse inverse regression estimators for sufficient variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    4. Zhou Yu & Yuexiao Dong & Li-Xing Zhu, 2016. "Trace Pursuit: A General Framework for Model-Free Variable Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 813-821, April.
    5. Radchenko, Peter, 2015. "High dimensional single index models," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 266-282.
    6. Tan, Xin Lu, 2019. "Optimal estimation of slope vector in high-dimensional linear transformation models," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 179-204.
    7. Hojin Yang & Hongtu Zhu & Joseph G. Ibrahim, 2018. "MILFM: Multiple index latent factor model based on high‐dimensional features," Biometrics, The International Biometric Society, vol. 74(3), pages 834-844, September.

  25. Guo, Xu & Zhu, Xuehu & Wong, Wing-Keung & Zhu, Lixing, 2013. "A note on almost stochastic dominance," Economics Letters, Elsevier, vol. 121(2), pages 252-256.
    See citations under working paper version above.
  26. Li, Gaorong & Lian, Heng & Feng, Sanying & Zhu, Lixing, 2013. "Automatic variable selection for longitudinal generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 174-186.

    Cited by:

    1. Zimu Chen & Zhanfeng Wang & Yuan‐chin Ivan Chang, 2020. "Sequential adaptive variables and subject selection for GEE methods," Biometrics, The International Biometric Society, vol. 76(2), pages 496-507, June.
    2. Tian, Ruiqin & Xue, Liugen & Xu, Dengke, 2016. "Automatic variable selection for varying coefficient models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 119(C), pages 84-90.
    3. Michael C. Knaus, 2018. "A Double Machine Learning Approach to Estimate the Effects of Musical Practice on Student's Skills," Papers 1805.10300, arXiv.org, revised Jan 2019.
    4. Kangning Wang & Wen Shan, 2021. "Copula and composite quantile regression-based estimating equations for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 441-455, June.
    5. Lv, Jing & Yang, Hu & Guo, Chaohui, 2015. "An efficient and robust variable selection method for longitudinal generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 74-88.
    6. Wang, Kangning & Li, Shaomin & Sun, Xiaofei & Lin, Lu, 2019. "Modal regression statistical inference for longitudinal data semivarying coefficient models: Generalized estimating equations, empirical likelihood and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 257-276.
    7. Geronimi, J. & Saporta, G., 2017. "Variable selection for multiply-imputed data with penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 103-114.
    8. Kangning Wang & Mengjie Hao & Xiaofei Sun, 2021. "Robust and efficient estimating equations for longitudinal data partial linear models and its applications," Statistical Papers, Springer, vol. 62(5), pages 2147-2168, October.

  27. Lin, Lu & Sun, Jing & Zhu, Lixing, 2013. "Nonparametric feature screening," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 162-174.

    Cited by:

    1. Qiu, Debin & Ahn, Jeongyoun, 2020. "Grouped variable screening for ultra-high dimensional data for linear model," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Yi Chu & Lu Lin, 2020. "Conditional SIRS for nonparametric and semiparametric models by marginal empirical likelihood," Statistical Papers, Springer, vol. 61(4), pages 1589-1606, August.
    3. Qinqin Hu & Lu Lin, 2017. "Conditional sure independence screening by conditional marginal empirical likelihood," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 63-96, February.
    4. Zhenghui Feng & Lu Lin & Ruoqing Zhu & Lixing Zhu, 2020. "Nonparametric variable selection and its application to additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 827-854, June.
    5. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Shuaishuai Chen & Jun Lu, 2023. "Quantile-Composited Feature Screening for Ultrahigh-Dimensional Data," Mathematics, MDPI, vol. 11(10), pages 1-21, May.
    7. Lin, Lu & Sun, Jing, 2016. "Adaptive conditional feature screening," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 287-301.
    8. Xiaolin Chen & Xiaojing Chen & Yi Liu, 2019. "A note on quantile feature screening via distance correlation," Statistical Papers, Springer, vol. 60(5), pages 1741-1762, October.
    9. Jun Lu & Lu Lin, 2020. "Model-free conditional screening via conditional distance correlation," Statistical Papers, Springer, vol. 61(1), pages 225-244, February.
    10. Zhou Yu & Yuexiao Dong & Li-Xing Zhu, 2016. "Trace Pursuit: A General Framework for Model-Free Variable Selection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 813-821, April.
    11. Lu, Jun & Lin, Lu & Wang, WenWu, 2021. "Partition-based feature screening for categorical data via RKHS embeddings," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    12. Peirong Xu & Lixing Zhu & Yi Li, 2014. "Ultrahigh dimensional time course feature selection," Biometrics, The International Biometric Society, vol. 70(2), pages 356-365, June.

  28. Zhenghui Feng & Xuerong Meggie Wen & Zhou Yu & Lixing Zhu, 2013. "On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 237-246, March.

    Cited by:

    1. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    2. Zeng, Bilin & Yu, Zhou & Wen, Xuerong Meggie, 2015. "A note on cumulative mean estimation," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 322-327.
    3. Xiaobing Zhao & Xian Zhou, 2020. "Partial sufficient dimension reduction on additive rates model for recurrent event data with high-dimensional covariates," Statistical Papers, Springer, vol. 61(2), pages 523-541, April.
    4. Ming-Yueh Huang & Kwun Chuen Gary Chan, 2022. "Model selection among Dimension-Reduced generalized Cox models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(3), pages 492-511, July.
    5. Ke, Chenlu & Yang, Wei & Yuan, Qingcong & Li, Lu, 2023. "Partial sufficient variable screening with categorical controls," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    6. Lu Li & Kai Tan & Xuerong Meggie Wen & Zhou Yu, 2023. "Variable-dependent partial dimension reduction," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(2), pages 521-541, June.
    7. Hilafu, Haileab & Wu, Wenbo, 2017. "Partial projective resampling method for dimension reduction: With applications to partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 1-14.
    8. Lu Li & Niwen Zhou & Lixing Zhu, 2022. "Outcome regression-based estimation of conditional average treatment effect," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(5), pages 987-1041, October.
    9. Ming-Yueh Huang & Kwun Chuen Gary Chan, 2024. "Adaptive Reduction of Curse of Dimensionality in Nonparametric Instrumental Variable Estimation," Mathematics, MDPI, vol. 13(1), pages 1-20, December.

  29. Yu, Zhou & Zhu, Lixing & Wen, Xuerong Meggie, 2012. "On model-free conditional coordinate tests for regressions," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 61-72.

    Cited by:

    1. Yu, Zhou & Dong, Yuexiao & Guo, Ranwei, 2013. "On determining the structural dimension via directional regression," Statistics & Probability Letters, Elsevier, vol. 83(4), pages 987-992.
    2. Liu, Xuejing & Yu, Zhou & Wen, Xuerong Meggie & Paige, Robert, 2015. "On testing common indices for two multi-index models: A link-free approach," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 75-85.
    3. Liu, Xuejing & Huo, Lei & Wen, Xuerong Meggie & Paige, Robert, 2017. "A link-free approach for testing common indices for three or more multi-index models," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 236-245.

  30. Jianhong Wu & Lixing Zhu, 2012. "Estimation of and testing for random effects in dynamic panel data models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 477-497, September.

    Cited by:

    1. Montes-Rojas Gabriel & Sosa-Escudero Walter & Zincenko Federico, 2020. "Level-Based Estimation of Dynamic Panel Models," Journal of Econometric Methods, De Gruyter, vol. 9(1), pages 1-23, January.
    2. Wu, Jianhong & Li, Guodong, 2014. "Moment-based tests for individual and time effects in panel data models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 569-581.
    3. Lu, Xun & Su, Liangjun, 2020. "Determining individual or time effects in panel data models," Journal of Econometrics, Elsevier, vol. 215(1), pages 60-83.

  31. Feng, Zhenghui & Zhu, Lixing, 2012. "An alternating determination–optimization approach for an additive multi-index model," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1981-1993.

    Cited by:

    1. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    2. Feng, Zhenghui & Wang, Tao & Zhu, Lixing, 2014. "Transformation-based estimation," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 186-205.
    3. Bilin Zeng & Xuerong Meggie Wen & Lixing Zhu, 2017. "A link-free sparse group variable selection method for single-index model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2388-2400, October.
    4. Liu, Xuejing & Yu, Zhou & Wen, Xuerong Meggie & Paige, Robert, 2015. "On testing common indices for two multi-index models: A link-free approach," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 75-85.

  32. Li, Gaorong & Lin, Lu & Zhu, Lixing, 2012. "Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 85-111.

    Cited by:

    1. Hong Guo & Changliang Zou & Zhaojun Wang & Bin Chen, 2014. "Empirical likelihood for high-dimensional linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(7), pages 921-945, October.
    2. Sanying Feng & Tiejun Tong & Sung Nok Chiu, 2023. "Statistical Inference for Partially Linear Varying Coefficient Spatial Autoregressive Panel Data Model," Mathematics, MDPI, vol. 11(22), pages 1-19, November.
    3. Jun Zhang & Yiping Yang & Gaorong Li, 2020. "Logarithmic calibration for multiplicative distortion measurement errors regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 462-488, November.
    4. Fan, Guo-Liang & Liang, Han-Ying & Shen, Yu, 2016. "Penalized empirical likelihood for high-dimensional partially linear varying coefficient model with measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 183-201.
    5. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    6. Li, Yujie & Li, Gaorong & Lian, Heng & Tong, Tiejun, 2017. "Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 133-150.
    7. Zhaoliang Wang & Liugen Xue & Gaorong Li & Fei Lu, 2019. "Spline estimator for ultra-high dimensional partially linear varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(3), pages 657-677, June.
    8. Wang, Kangning & Li, Shaomin & Sun, Xiaofei & Lin, Lu, 2019. "Modal regression statistical inference for longitudinal data semivarying coefficient models: Generalized estimating equations, empirical likelihood and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 257-276.
    9. Sanying Feng & Liugen Xue, 2014. "Bias-corrected statistical inference for partially linear varying coefficient errors-in-variables models with restricted condition," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 121-140, February.
    10. Bang-Qiang He & Xing-Jian Hong & Guo-Liang Fan, 2020. "Penalized empirical likelihood for partially linear errors-in-variables panel data models with fixed effects," Statistical Papers, Springer, vol. 61(6), pages 2351-2381, December.
    11. Zhang, Jun & Feng, Zhenghui & Zhou, Bu, 2014. "A revisit to correlation analysis for distortion measurement error data," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 116-129.
    12. Yang, Yiping & Li, Gaorong & Peng, Heng, 2014. "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 1-18.
    13. Zhenghui Feng & Jun Zhang & Qian Chen, 2020. "Statistical inference for linear regression models with additive distortion measurement errors," Statistical Papers, Springer, vol. 61(6), pages 2483-2509, December.

  33. Xu, Peirong & Zhu, Lixing, 2012. "Estimation for a marginal generalized single-index longitudinal model," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 285-299.

    Cited by:

    1. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    2. Jing Lv & Chaohui Guo, 2017. "Efficient parameter estimation via modified Cholesky decomposition for quantile regression with longitudinal data," Computational Statistics, Springer, vol. 32(3), pages 947-975, September.
    3. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    4. Shakhawat Hossain & Le An Lac, 2021. "Optimal shrinkage estimations in partially linear single-index models for binary longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 811-835, December.
    5. Jiang, Rong & Zhou, Zhan-Gong & Qian, Wei-Min & Chen, Yong, 2013. "Two step composite quantile regression for single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 180-191.
    6. Lv, Jing & Yang, Hu & Guo, Chaohui, 2015. "An efficient and robust variable selection method for longitudinal generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 74-88.
    7. Jing Lv & Chaohui Guo, 2019. "Quantile estimations via modified Cholesky decomposition for longitudinal single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(5), pages 1163-1199, October.
    8. Peirong Xu & Jun Zhang & Xingfang Huang & Tao Wang, 2016. "Efficient estimation for marginal generalized partially linear single-index models with longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 413-431, September.
    9. Yongjin Li & Qingzhao Zhang & Qihua Wang, 2017. "Penalized estimation equation for an extended single-index model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 169-187, February.
    10. Rong Jiang & Wei-Min Qian & Zhan-Gong Zhou, 2016. "Single-index composite quantile regression with heteroscedasticity and general error distributions," Statistical Papers, Springer, vol. 57(1), pages 185-203, March.
    11. Xu, Peirong & Peng, Heng & Huang, Tao, 2018. "Unsupervised learning of mixture regression models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 44-56.

  34. Wang, Tao & Zhu, Lixing, 2011. "Consistent tuning parameter selection in high dimensional sparse linear regression," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1141-1151, August.

    Cited by:

    1. Zhongzhe Ouyang & Lu Wang & Alzheimer’s Disease Neuroimaging Initiative, 2024. "Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies," Mathematics, MDPI, vol. 12(7), pages 1-14, March.
    2. Yingying Fan & Cheng Yong Tang, 2013. "Tuning parameter selection in high dimensional penalized likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 531-552, June.
    3. Chen, Yunxiao & Li, Xiaoou & Liu, Jingchen & Ying, Zhiliang, 2017. "Regularized latent class analysis with application in cognitive diagnosis," LSE Research Online Documents on Economics 103182, London School of Economics and Political Science, LSE Library.
    4. Yunxiao Chen & Xiaoou Li & Jingchen Liu & Zhiliang Ying, 2017. "Regularized Latent Class Analysis with Application in Cognitive Diagnosis," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 660-692, September.
    5. David Degras, 2021. "Sparse group fused lasso for model segmentation: a hybrid approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 625-671, September.
    6. Burman, Prabir & Paul, Debashis, 2017. "Smooth predictive model fitting in regression," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 165-179.
    7. Jack Jewson & Li Li & Laura Battaglia & Stephen Hansen & David Rossell & Piotr Zwiernik, 2022. "Graphical model inference with external network data," CeMMAP working papers 20/22, Institute for Fiscal Studies.
    8. Ardia, David & Bluteau, Keven & Boudt, Kris, 2019. "Questioning the news about economic growth: Sparse forecasting using thousands of news-based sentiment values," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1370-1386.
    9. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    10. Zhang, Tonglin, 2024. "Variables selection using L0 penalty," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).

  35. Lexin Li & Liping Zhu & Lixing Zhu, 2011. "Inference on the primary parameter of interest with the aid of dimension reduction estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 59-80, January.

    Cited by:

    1. Hilafu, Haileab & Wu, Wenbo, 2017. "Partial projective resampling method for dimension reduction: With applications to partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 1-14.
    2. Wang, Qihua & Su, Miaomiao & Wang, Ruoyu, 2021. "A beyond multiple robust approach for missing response problem," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).

  36. Jinhong You & Xian Zhou & Lixing Zhu & Bin Zhou, 2011. "Weighted denoised minimum distance estimation in a regression model with autocorrelated measurement errors," Statistical Papers, Springer, vol. 52(2), pages 263-286, May.

    Cited by:

    1. Sukhbir Singh & Kanchan Jain & Suresh Sharma, 2014. "Replicated measurement error model under exact linear restrictions," Statistical Papers, Springer, vol. 55(2), pages 253-274, May.

  37. Wu, Jianhong & Zhu, Lixing, 2011. "Testing for serial correlation and random effects in a two-way error component regression model," Economic Modelling, Elsevier, vol. 28(6), pages 2377-2386.

    Cited by:

    1. Wu, Jianhong, 2016. "Robust random effects tests for two-way error component models with panel data," Economic Modelling, Elsevier, vol. 59(C), pages 1-8.
    2. Wu, Jianhong, 2020. "A joint test for serial correlation and heteroscedasticity in fixed-T panel regression models with interactive effects," Economics Letters, Elsevier, vol. 197(C).

  38. Zhu, Lixing & Lin, Lu & Cui, Xia & Li, Gaorong, 2010. "Bias-corrected empirical likelihood in a multi-link semiparametric model," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 850-868, April.

    Cited by:

    1. Zhang, Jun & Gai, Yujie & Wu, Ping, 2013. "Estimation in linear regression models with measurement errors subject to single-indexed distortion," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 103-120.
    2. Weihua Zhao & Jianbo Li & Heng Lian, 2018. "Adaptive varying-coefficient linear quantile model: a profiled estimating equations approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 553-582, June.
    3. Li, Gaorong & Feng, Sanying & Peng, Heng, 2011. "A profile-type smoothed score function for a varying coefficient partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 372-385, February.
    4. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    5. Matsushita, Yukitoshi & Otsu, Taisuke, 2020. "Likelihood inference on semiparametric models with generated regressors," LSE Research Online Documents on Economics 102696, London School of Economics and Political Science, LSE Library.
    6. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Jun Zhang & Zhenghui Feng & Peirong Xu, 2015. "Estimating the conditional single-index error distribution with a partial linear mean regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 61-83, March.
    8. Zhang, Jun & Feng, Zhenghui & Zhou, Bu, 2014. "A revisit to correlation analysis for distortion measurement error data," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 116-129.

  39. Chang, Ziqing & Xue, Liugen & Zhu, Lixing, 2010. "On an asymptotically more efficient estimation of the single-index model," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1898-1901, September.

    Cited by:

    1. Zhensheng Huang & Xing Sun & Riquan Zhang, 2022. "Estimation for partially varying-coefficient single-index models with distorted measurement errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 175-201, February.
    2. Qihua Wang & Tao Zhang & Wolfgang Karl Härdle, 2016. "An Extended Single-index Model with Missing Response at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1140-1152, December.
    3. Ke Wang & Dehui Wang, 2024. "Estimation for partially linear single-index spatial autoregressive model with covariate measurement errors," Statistical Papers, Springer, vol. 65(7), pages 4201-4241, September.
    4. Yang, Hu & Guo, Chaohui & Lv, Jing, 2014. "A robust and efficient estimation method for single-index varying-coefficient models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 119-127.
    5. Lai, Peng & Wang, Qihua & Lian, Heng, 2012. "Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 422-432.
    6. Claudio Agostinelli & Ana M. Bianco & Graciela Boente, 2020. "Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 855-893, June.
    7. Huang, Zhensheng & Zhang, Riquan, 2011. "Efficient empirical-likelihood-based inferences for the single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(5), pages 937-947, May.
    8. Guo, Xu & Xu, Wangli & Zhu, Lixing, 2014. "Multi-index regression models with missing covariates at random," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 345-363.
    9. Yiping Yang & Tiejun Tong & Gaorong Li, 2019. "SIMEX estimation for single-index model with covariate measurement error," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 137-161, March.

  40. Zaixing Li & Lixing Zhu, 2010. "On Variance Components in Semiparametric Mixed Models for Longitudinal Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(3), pages 442-457, September.

    Cited by:

    1. Zaixing Li & Fei Chen & Lixing Zhu, 2014. "Variance Components Testing in ANOVA-Type Mixed Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(2), pages 482-496, June.
    2. M. Taavoni & M. Arashi, 2021. "Kernel estimation in semiparametric mixed effect longitudinal modeling," Statistical Papers, Springer, vol. 62(3), pages 1095-1116, June.
    3. Chen, Fei & Li, Zaixing & Shi, Lei & Zhu, Lixing, 2015. "Inference for mixed models of ANOVA type with high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 382-401.
    4. Corinne Emmenegger & Peter Bühlmann, 2023. "Plug‐in machine learning for partially linear mixed‐effects models with repeated measurements," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(4), pages 1553-1567, December.
    5. Wu, Jianhong & Li, Guodong, 2014. "Moment-based tests for individual and time effects in panel data models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 569-581.
    6. Li, Zaixing, 2015. "A residual-based test for variance components in linear mixed models," Statistics & Probability Letters, Elsevier, vol. 98(C), pages 73-78.
    7. Jianhong Wu & Lixing Zhu, 2012. "Estimation of and testing for random effects in dynamic panel data models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 477-497, September.
    8. Zaixing Li, 2017. "Inference of nonlinear mixed models for clustered data under moment conditions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 759-781, December.
    9. Zaixing Li, 2013. "Two kinds of variance/covariance estimates in linear mixed models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(3), pages 303-324, April.
    10. Cibele Russo & Reiko Aoki & Gilberto Paula, 2012. "Assessment of variance components in nonlinear mixed-effects elliptical models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(3), pages 519-545, September.

  41. Li, Gaorong & Zhu, Lixing & Xue, Liugen & Feng, Sanying, 2010. "Empirical likelihood inference in partially linear single-index models for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 718-732, March.

    Cited by:

    1. Zhang, Jun & Gai, Yujie & Wu, Ping, 2013. "Estimation in linear regression models with measurement errors subject to single-indexed distortion," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 103-120.
    2. Jun Zhang & Zhenghui Feng & Xiaoguang Wang, 2018. "A constructive hypothesis test for the single-index models with two groups," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1077-1114, October.
    3. Lian, Heng & Liang, Hua, 2016. "Separation of linear and index covariates in partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 56-70.
    4. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    5. Holland, Ashley D., 2017. "Penalized spline estimation in the partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 211-235.
    6. Li, Gaorong & Feng, Sanying & Peng, Heng, 2011. "A profile-type smoothed score function for a varying coefficient partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 372-385, February.
    7. Zhang, Junhua & Feng, Sanying & Li, Gaorong & Lian, Heng, 2011. "Empirical likelihood inference for partially linear panel data models with fixed effects," Economics Letters, Elsevier, vol. 113(2), pages 165-167.
    8. Li, Gao-Rong & Zhu, Li-Ping & Zhu, Li-Xing, 2010. "Adaptive confidence region for the direction in semiparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1364-1377, July.
    9. Shakhawat Hossain & Le An Lac, 2021. "Optimal shrinkage estimations in partially linear single-index models for binary longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 811-835, December.
    10. Lai, Peng & Wang, Qihua & Lian, Heng, 2012. "Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 422-432.
    11. Li, Daoji & Pan, Jianxin, 2013. "Empirical likelihood for generalized linear models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 63-73.
    12. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    13. Lai, Peng & Li, Gaorong & Lian, Heng, 2013. "Quadratic inference functions for partially linear single-index models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 115-127.
    14. Yang, Suigen & Xue, Liugen & Li, Gaorong, 2014. "Simultaneous confidence band for single-index random effects models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 6-14.
    15. Peirong Xu & Jun Zhang & Xingfang Huang & Tao Wang, 2016. "Efficient estimation for marginal generalized partially linear single-index models with longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 413-431, September.
    16. Changsheng Liu & Hanying Liang & Yongmei Li, 2025. "Bayesian quantile regression for partially linear single-index model with longitudinal data," Statistical Papers, Springer, vol. 66(1), pages 1-51, January.
    17. Li, Gaorong & Lin, Lu & Zhu, Lixing, 2012. "Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 85-111.
    18. Yang, Yiping & Li, Gaorong & Peng, Heng, 2014. "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 1-18.
    19. Zhang, Yuexia & Qin, Guoyou & Zhu, Zhongyi & Xu, Wanghong, 2019. "A novel robust approach for analysis of longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 83-95.

  42. Qiang Chen & Lu Lin & Lixing Zhu, 2010. "Bias-corrected smoothed score function for single-index models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(1), pages 45-58, January.

    Cited by:

    1. Li, Gaorong & Feng, Sanying & Peng, Heng, 2011. "A profile-type smoothed score function for a varying coefficient partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 372-385, February.
    2. Zhao, Weihua & Lian, Heng & Zhang, Riquan & Lai, Peng, 2016. "Estimation and variable selection for proportional response data with partially linear single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 40-56.

  43. Liping Zhu & Tao Wang & Lixing Zhu & Louis Ferré, 2010. "Sufficient dimension reduction through discretization-expectation estimation," Biometrika, Biometrika Trust, vol. 97(2), pages 295-304.

    Cited by:

    1. Wang, Tao & Zhu, Lixing, 2013. "Sparse sufficient dimension reduction using optimal scoring," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 223-232.
    2. Zeng, Bilin & Yu, Zhou & Wen, Xuerong Meggie, 2015. "A note on cumulative mean estimation," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 322-327.
    3. Deng, Jianqiu & Yang, Xiaojie & Wang, Qihua, 2022. "Surrogate space based dimension reduction for nonignorable nonresponse," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    4. Feng, Zhenghui & Wang, Tao & Zhu, Lixing, 2014. "Transformation-based estimation," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 186-205.
    5. Wang, Lei & Zhao, Puying & Shao, Jun, 2021. "Dimension-reduced semiparametric estimation of distribution functions and quantiles with nonignorable nonresponse," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    6. Xinchao Luo & Lixing Zhu & Hongtu Zhu, 2016. "Single‐index varying coefficient model for functional responses," Biometrics, The International Biometric Society, vol. 72(4), pages 1275-1284, December.
    7. Hongxia Wang & Zihan Zhao & Hongxia Hao & Chao Huang, 2023. "Estimation and Inference for Spatio-Temporal Single-Index Models," Mathematics, MDPI, vol. 11(20), pages 1-32, October.
    8. Zhenghui Feng & Lu Lin & Ruoqing Zhu & Lixing Zhu, 2020. "Nonparametric variable selection and its application to additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 827-854, June.
    9. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Yu, Zhou & Zhu, Lixing & Wen, Xuerong Meggie, 2012. "On model-free conditional coordinate tests for regressions," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 61-72.
    11. Siming Zheng & Alan T. K. Wan & Yong Zhou, 2024. "Semiparametric recovery of central dimension reduction space with nonignorable nonresponse," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 78(2), pages 374-396, May.
    12. Hung Hung & Su‐Yun Huang, 2019. "Sufficient dimension reduction via random‐partitions for the large‐p‐small‐n problem," Biometrics, The International Biometric Society, vol. 75(1), pages 245-255, March.
    13. Hilafu, Haileab & Wu, Wenbo, 2017. "Partial projective resampling method for dimension reduction: With applications to partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 1-14.
    14. Zhu, Xuehu & Chen, Fei & Guo, Xu & Zhu, Lixing, 2016. "Heteroscedasticity testing for regression models: A dimension reduction-based model adaptive approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 263-283.
    15. Wang, Qin & Xue, Yuan, 2021. "An ensemble of inverse moment estimators for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    16. Wang, Tao & Xu, Pei-Rong & Zhu, Li-Xing, 2012. "Non-convex penalized estimation in high-dimensional models with single-index structure," Journal of Multivariate Analysis, Elsevier, vol. 109(C), pages 221-235.
    17. Lili Xia & Tingyu Lai & Zhongzhan Zhang, 2023. "An Adaptive-to-Model Test for Parametric Functional Single-Index Model," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    18. Zhu, Xuehu & Guo, Xu & Lin, Lu & Zhu, Lixing, 2015. "Heteroscedasticity checks for single index models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 41-55.
    19. Lei Wang, 2019. "Dimension reduction for kernel-assisted M-estimators with missing response at random," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 889-910, August.
    20. Matilainen, M. & Croux, C. & Nordhausen, K. & Oja, H., 2017. "Supervised dimension reduction for multivariate time series," Econometrics and Statistics, Elsevier, vol. 4(C), pages 57-69.
    21. Xie, Chuanlong & Zhu, Lixing, 2019. "A goodness-of-fit test for variable-adjusted models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 27-48.
    22. Chen, Feifei & Jiang, Qing & Feng, Zhenghui & Zhu, Lixing, 2020. "Model checks for functional linear regression models based on projected empirical processes," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    23. Zhang, Jun & Zhu, Li-Ping & Zhu, Li-Xing, 2012. "On a dimension reduction regression with covariate adjustment," Journal of Multivariate Analysis, Elsevier, vol. 104(1), pages 39-55, February.
    24. Guo, Xu & Wang, Tao & Xu, Wangli & Zhu, Lixing, 2014. "Dimension reduction with missing response at random," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 228-242.
    25. Feng, Zhenghui & Zhu, Lixing, 2012. "An alternating determination–optimization approach for an additive multi-index model," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1981-1993.
    26. Zhu, Xuehu & Guo, Xu & Wang, Tao & Zhu, Lixing, 2020. "Dimensionality determination: A thresholding double ridge ratio approach," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).
    27. Nordhausen, Klaus & Oja, Hannu & Tyler, David E., 2022. "Asymptotic and bootstrap tests for subspace dimension," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    28. Lian, Heng & Li, Gaorong, 2014. "Series expansion for functional sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 150-165.
    29. Zeng, Yicheng & Zhu, Lixing, 2023. "Order determination for spiked-type models with a divergent number of spikes," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    30. Xie, Chuanlong & Zhu, Lixing, 2020. "Generalized kernel-based inverse regression methods for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    31. Guo, Xu & Xu, Wangli & Zhu, Lixing, 2014. "Multi-index regression models with missing covariates at random," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 345-363.
    32. Wei Luo, 2022. "On efficient dimension reduction with respect to the interaction between two response variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 269-294, April.
    33. Lin, Lu & Sun, Jing & Zhu, Lixing, 2013. "Nonparametric feature screening," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 162-174.
    34. Zhou, Jingke & Xu, Wangli & Zhu, Lixing, 2015. "Robust estimating equation-based sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 99-118.
    35. Cuizhen Niu & Lixing Zhu, 2018. "A robust adaptive-to-model enhancement test for parametric single-index models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(5), pages 1013-1045, October.
    36. Zhou, Jingke & Zhu, Lixing, 2016. "Principal minimax support vector machine for sufficient dimension reduction with contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 33-48.

  44. Li, Zaixing & Xu, Wangli & Zhu, Lixing, 2009. "Influence diagnostics and outlier tests for varying coefficient mixed models," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2002-2017, October.

    Cited by:

    1. Zhao, Yan-Yong & Lin, Jin-Guan & Xu, Pei-Rong & Ye, Xu-Guo, 2015. "Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 204-221.

  45. Zhu, L. & Hurt, R. & Correa, D. & Boehm, R., 2009. "Comprehensive energy and economic analyses on a zero energy house versus a conventional house," Energy, Elsevier, vol. 34(9), pages 1043-1053.

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    1. Pacheco, Miguel & Lamberts, Roberto, 2013. "Assessment of technical and economical viability for large-scale conversion of single family residential buildings into zero energy buildings in Brazil: Climatic and cultural considerations," Energy Policy, Elsevier, vol. 63(C), pages 716-725.
    2. Fong, K.F. & Lee, C.K., 2012. "Towards net zero energy design for low-rise residential buildings in subtropical Hong Kong," Applied Energy, Elsevier, vol. 93(C), pages 686-694.
    3. Diallo, Arouna & Moussa, Richard K., 2020. "The effects of solar home system on welfare in off-grid areas: Evidence from Côte d’Ivoire," Energy, Elsevier, vol. 194(C).
    4. Berry, Stephen & Davidson, Kathryn, 2016. "Improving the economics of building energy code change: A review of the inputs and assumptions of economic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 157-166.
    5. Clune, Stephen & Morrissey, John & Moore, Trivess, 2012. "Size matters: House size and thermal efficiency as policy strategies to reduce net emissions of new developments," Energy Policy, Elsevier, vol. 48(C), pages 657-667.
    6. He, Hongming & Jim, C.Y., 2010. "Simulation of thermodynamic transmission in green roof ecosystem," Ecological Modelling, Elsevier, vol. 221(24), pages 2949-2958.
    7. Jalilinasrabady, Saeid & Palsson, Halldor & Saevarsdottir, Gudrun & Itoi, Ryuichi & Valdimarsson, Pall, 2013. "Experimental and CFD simulation of heat efficiency improvement in geothermal spas," Energy, Elsevier, vol. 56(C), pages 124-134.
    8. Desideri, Umberto & Arcioni, Livia & Leonardi, Daniela & Cesaretti, Luca & Perugini, Perla & Agabitini, Elena & Evangelisti, Nicola, 2013. "Design of a multipurpose “zero energy consumption” building according to European Directive 2010/31/EU: Architectural and technical plants solutions," Energy, Elsevier, vol. 58(C), pages 157-167.
    9. Wang, Yang & Zhao, Fu-Yun & Kuckelkorn, Jens & Liu, Di & Liu, Li-Qun & Pan, Xiao-Chuan, 2014. "Cooling energy efficiency and classroom air environment of a school building operated by the heat recovery air conditioning unit," Energy, Elsevier, vol. 64(C), pages 991-1001.
    10. Li, Danny H.W. & Yang, Liu & Lam, Joseph C., 2013. "Zero energy buildings and sustainable development implications – A review," Energy, Elsevier, vol. 54(C), pages 1-10.
    11. Meng, Xiangxin & Yang, Liu & Wang, Shangyu & Lyu, Kailin & Cao, Qimeng & Liu, Yan, 2024. "Methods of determining thresholds of climate variables in building thermal design zoning in China," Energy, Elsevier, vol. 308(C).
    12. Zhou, Zhihua & Feng, Lei & Zhang, Shuzhen & Wang, Chendong & Chen, Guanyi & Du, Tao & Li, Yasong & Zuo, Jian, 2016. "The operational performance of “net zero energy building”: A study in China," Applied Energy, Elsevier, vol. 177(C), pages 716-728.
    13. Liu, Zhijian & Liu, Yuanwei & He, Bao-Jie & Xu, Wei & Jin, Guangya & Zhang, Xutao, 2019. "Application and suitability analysis of the key technologies in nearly zero energy buildings in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 329-345.
    14. Franzitta, Vincenzo & La Gennusa, Maria & Peri, Giorgia & Rizzo, Gianfranco & Scaccianoce, Gianluca, 2011. "Toward a European Eco-label brand for residential buildings: Holistic or by-components approaches?," Energy, Elsevier, vol. 36(4), pages 1884-1892.
    15. Kapsalis, Vasileios & Maduta, Carmen & Skandalos, Nikolaos & Wang, Meng & Bhuvad, Sushant Suresh & D'Agostino, Delia & Ma, Tao & Raj, Uday & Parker, Danny & Peng, Jinqing & Karamanis, Dimitris, 2024. "Critical assessment of large-scale rooftop photovoltaics deployment in the global urban environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    16. Niu, Shu-wen & Li, Yi-xin & Ding, Yong-xia & Qin, Jing, 2010. "Energy demand for rural household heating to suitable levels in the Loess Hilly Region, Gansu Province, China," Energy, Elsevier, vol. 35(5), pages 2070-2078.
    17. Xing, Yangang & Hewitt, Neil & Griffiths, Philip, 2011. "Zero carbon buildings refurbishment--A Hierarchical pathway," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 3229-3236, August.
    18. Mishra, Pulak & Behera, Bhagirath, 2016. "Socio-economic and environmental implications of solar electrification: Experience of rural Odisha," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 953-964.
    19. de Rubeis, Tullio & Nardi, Iole & Ambrosini, Dario & Paoletti, Domenica, 2018. "Is a self-sufficient building energy efficient? Lesson learned from a case study in Mediterranean climate," Applied Energy, Elsevier, vol. 218(C), pages 131-145.
    20. Li, Xuesong & Li, Hao & Wang, Xingwu, 2013. "Farmers' willingness to convert traditional houses to solar houses in rural areas: A survey of 465 households in Chongqing, China," Energy Policy, Elsevier, vol. 63(C), pages 882-886.
    21. Meng, Xiangxin & Liu, Yan & Wang, Shangyu & Chen, Feiyu & Cao, Qimeng & Yang, Liu, 2022. "A fast solar architecture design method towards zero heating energy: A SHF-SLR-based model and its parameters," Energy, Elsevier, vol. 258(C).

  46. Wangli Xu & Lixing Zhu, 2009. "Kernel‐based Generalized Cross‐validation in Non‐parametric Mixed‐effect Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 229-247, June.

    Cited by:

    1. H. Poulos, 2010. "Spatially explicit mapping of hurricane risk in New England, USA using ArcGIS," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 54(3), pages 1015-1023, September.
    2. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    3. Zhao, Yan-Yong & Lin, Jin-Guan & Xu, Pei-Rong & Ye, Xu-Guo, 2015. "Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 204-221.
    4. González Manteiga, Wenceslao & Lombardía, María José & Martínez Miranda, María Dolores & Sperlich, Stefan, 2013. "Kernel smoothers and bootstrapping for semiparametric mixed effects models," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 288-302.

  47. Lu Lin & Xia Cui & Lixing Zhu, 2009. "An Adaptive Two‐stage Estimation Method for Additive Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 248-269, June.

    Cited by:

    1. Lin, Lu & Song, Yunquan & Liu, Zhao, 2014. "Local linear–additive estimation for multiple nonparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 252-269.
    2. Zhenghui Feng & Lu Lin & Ruoqing Zhu & Lixing Zhu, 2020. "Nonparametric variable selection and its application to additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 827-854, June.
    3. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

  48. Li, Bing & Wen, Songqiao & Zhu, Lixing, 2008. "On a Projective Resampling Method for Dimension Reduction With Multivariate Responses," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 1177-1186.

    Cited by:

    1. Coudret, R. & Girard, S. & Saracco, J., 2014. "A new sliced inverse regression method for multivariate response," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 285-299.
    2. Zeng, Bilin & Yu, Zhou & Wen, Xuerong Meggie, 2015. "A note on cumulative mean estimation," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 322-327.
    3. Iaci, Ross & Yin, Xiangrong & Zhu, Lixing, 2016. "The Dual Central Subspaces in dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 178-189.
    4. Hilafu, Haileab & Yin, Xiangrong, 2013. "Sufficient dimension reduction in multivariate regressions with categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 139-147.
    5. Girard, Stéphane & Lorenzo, Hadrien & Saracco, Jérôme, 2022. "Advanced topics in Sliced Inverse Regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    6. Weng, Jiaying, 2022. "Fourier transform sparse inverse regression estimators for sufficient variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    7. Zhang, Hong-Fan, 2021. "Minimum Average Variance Estimation with group Lasso for the multivariate response Central Mean Subspace," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    8. Hilafu, Haileab & Wu, Wenbo, 2017. "Partial projective resampling method for dimension reduction: With applications to partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 1-14.
    9. Zhu, Xuehu & Chen, Fei & Guo, Xu & Zhu, Lixing, 2016. "Heteroscedasticity testing for regression models: A dimension reduction-based model adaptive approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 263-283.
    10. Xue, Yuan & Yin, Xiangrong & Jiang, Xiaolin, 2016. "Ensemble sufficient dimension folding methods for analyzing matrix-valued data," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 193-205.
    11. Heng-Hui Lue, 2010. "On principal Hessian directions for multivariate response regressions," Computational Statistics, Springer, vol. 25(4), pages 619-632, December.
    12. Jae Yoo & Keunbaik Lee & Seongho Wu, 2010. "On the extension of sliced average variance estimation to multivariate regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(4), pages 529-540, November.
    13. Xie, Chuanlong & Zhu, Lixing, 2019. "A goodness-of-fit test for variable-adjusted models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 27-48.
    14. Luo, Chongliang & Liang, Jian & Li, Gen & Wang, Fei & Zhang, Changshui & Dey, Dipak K. & Chen, Kun, 2018. "Leveraging mixed and incomplete outcomes via reduced-rank modeling," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 378-394.
    15. Zhang, Wei & Gao, Wei & Ng, Hon Keung Tony, 2023. "Multivariate tests of independence based on a new class of measures of independence in Reproducing Kernel Hilbert Space," Journal of Multivariate Analysis, Elsevier, vol. 195(C).
    16. Zhang, Yaowu & Zhu, Liping & Ma, Yanyuan, 2017. "Efficient dimension reduction for multivariate response data," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 187-199.
    17. Wang, Pei & Yin, Xiangrong & Yuan, Qingcong & Kryscio, Richard, 2021. "Feature filter for estimating central mean subspace and its sparse solution," Computational Statistics & Data Analysis, Elsevier, vol. 163(C).
    18. Sheng, Wenhui & Yin, Xiangrong, 2013. "Direction estimation in single-index models via distance covariance," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 148-161.

  49. Liugen Xue & Lixing Zhu, 2007. "Empirical Likelihood Semiparametric Regression Analysis for Longitudinal Data," Biometrika, Biometrika Trust, vol. 94(4), pages 921-937.

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    1. Francesco Bravo, 2014. "Varying coefficients partially linear models with randomly censored data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(2), pages 383-412, April.
    2. Xingcai Zhou & Dehan Kong & Matthew Stephen Pietrosanu & Linglong Kong & Rohana J. Karunamuni, 2024. "Empirical likelihood M‐estimation for the varying‐coefficient model with functional response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(3), pages 1357-1387, September.
    3. Qian, Lianfen & Wang, Suojin, 2017. "Subject-wise empirical likelihood inference in partial linear models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 77-87.
    4. Peixin Zhao & Xinrong Tang, 2016. "Imputation based statistical inference for partially linear quantile regression models with missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 991-1009, November.
    5. Peixin Zhao & Liugen Xue, 2009. "Empirical likelihood inferences for semiparametric varying-coefficient partially linear errors-in-variables models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 907-923.
    6. Shuanghua Luo & Yuxin Yan & Cheng-yi Zhang, 2024. "Two-Stage Estimation of Partially Linear Varying Coefficient Quantile Regression Model with Missing Data," Mathematics, MDPI, vol. 12(4), pages 1-15, February.
    7. Lei Wang & Wei Ma, 2021. "Improved empirical likelihood inference and variable selection for generalized linear models with longitudinal nonignorable dropouts," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 623-647, June.
    8. Li, Daoji & Pan, Jianxin, 2013. "Empirical likelihood for generalized linear models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 63-73.
    9. Qin, Guoyou & Bai, Yang & Zhu, Zhongyi, 2012. "Robust empirical likelihood inference for generalized partial linear models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 32-44.
    10. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    11. Li, Gaorong & Zhu, Lixing & Xue, Liugen & Feng, Sanying, 2010. "Empirical likelihood inference in partially linear single-index models for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 718-732, March.
    12. Lai, Peng & Li, Gaorong & Lian, Heng, 2013. "Quadratic inference functions for partially linear single-index models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 115-127.
    13. Wang, Kangning & Li, Shaomin & Sun, Xiaofei & Lin, Lu, 2019. "Modal regression statistical inference for longitudinal data semivarying coefficient models: Generalized estimating equations, empirical likelihood and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 257-276.
    14. Peixin Zhao & Xiaoshuang Zhou, 2018. "Robust empirical likelihood for partially linear models via weighted composite quantile regression," Computational Statistics, Springer, vol. 33(2), pages 659-674, June.
    15. Kangning Wang & Mengjie Hao & Xiaofei Sun, 2021. "Robust and efficient estimating equations for longitudinal data partial linear models and its applications," Statistical Papers, Springer, vol. 62(5), pages 2147-2168, October.
    16. Bindele, Huybrechts F. & Abebe, Ash, 2015. "Semi-parametric rank regression with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 117-132.
    17. Xue, Liugen, 2009. "Empirical likelihood for linear models with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1353-1366, August.
    18. Qin, Guoyou & Bai, Yang & Zhu, Zhongyi, 2009. "Robust empirical likelihood inference for longitudinal data," Statistics & Probability Letters, Elsevier, vol. 79(20), pages 2101-2108, October.
    19. Yang, Hu & Li, Tingting, 2010. "Empirical likelihood for semiparametric varying coefficient partially linear models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 80(2), pages 111-121, January.
    20. Yang, Yiping & Li, Gaorong & Peng, Heng, 2014. "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 1-18.
    21. He, Bang-Qiang & Hong, Xing-Jian & Fan, Guo-Liang, 2017. "Block empirical likelihood for partially linear panel data models with fixed effects," Statistics & Probability Letters, Elsevier, vol. 123(C), pages 128-138.
    22. Xiaoshuang Zhou & Peixin Zhao & Yujie Gai, 2022. "Imputation-based empirical likelihood inferences for partially nonlinear quantile regression models with missing responses," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(4), pages 705-722, December.
    23. Xue, Liugen & Xue, Dong, 2011. "Empirical likelihood for semiparametric regression model with missing response data," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 723-740, April.
    24. Hengzhen Huang & Guangni Mo & Haiou Li & Hong-Bin Fang, 2022. "Representation Theorem and Functional CLT for RKHS-Based Function-on-Function Regressions," Mathematics, MDPI, vol. 10(14), pages 1-23, July.
    25. Peixin Zhao & Liugen Xue, 2012. "Variable selection in semiparametric regression analysis for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 213-231, February.
    26. Zhao, Peixin & Xue, Liugen, 2010. "Variable selection for semiparametric varying coefficient partially linear errors-in-variables models," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1872-1883, September.

  50. Xue, Liugen & Zhu, Lixing, 2007. "Empirical Likelihood for a Varying Coefficient Model With Longitudinal Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 642-654, June.

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    1. Yan-Yong Zhao & Jin-Guan Lin & Hong-Xia Wang & Xing-Fang Huang, 2017. "Jump-detection-based estimation in time-varying coefficient models and empirical applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 574-599, September.
    2. Gong, Yun & Peng, Liang & Qi, Yongcheng, 2010. "Smoothed jackknife empirical likelihood method for ROC curve," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1520-1531, July.
    3. Liugen Xue, 2010. "Empirical Likelihood Local Polynomial Regression Analysis of Clustered Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 644-663, December.
    4. Yan-Yong Zhao & Ling-Ling Ge & Yuan Liu, 2025. "Estimation of panel data partially linear time-varying coefficient models with cross-sectional spatial autoregressive errors," Statistical Papers, Springer, vol. 66(1), pages 1-37, January.
    5. Arteaga-Molina, Luis A. & Rodríguez-Poo, Juan M., 2019. "Empirical likelihood based inference for a categorical varying-coefficient panel data model with fixed effects," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 110-124.
    6. Lijie Gu & Li Wang & Wolfgang Härdle & Lijian Yang, 2014. "A simultaneous confidence corridor for varying coefficient regression with sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(4), pages 806-843, December.
    7. Huang, Zhensheng & Pang, Zhen, 2012. "Corrected empirical likelihood inference for right-censored partially linear single-index model," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 276-284.
    8. Peixin Zhao & Liugen Xue, 2013. "Instrumental variable-based empirical likelihood inferences for varying-coefficient models with error-prone covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(2), pages 380-396, February.
    9. Xu, Ke-Li, 2020. "Inference of local regression in the presence of nuisance parameters," Journal of Econometrics, Elsevier, vol. 218(2), pages 532-560.
    10. Peixin Zhao & Xinrong Tang, 2016. "Imputation based statistical inference for partially linear quantile regression models with missing responses," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 991-1009, November.
    11. Zhensheng Huang, 2011. "Empirical likelihood for generalized partially linear varying-coefficient models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(6), pages 1265-1275, May.
    12. Peixin Zhao & Liugen Xue, 2009. "Empirical likelihood inferences for semiparametric varying-coefficient partially linear errors-in-variables models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(7), pages 907-923.
    13. Li, Gao-Rong & Zhu, Li-Ping & Zhu, Li-Xing, 2010. "Adaptive confidence region for the direction in semiparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1364-1377, July.
    14. Qiang Chen & Lu Lin & Lixing Zhu, 2010. "Bias-corrected smoothed score function for single-index models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(1), pages 45-58, January.
    15. Xiuli Wang & Gaorong Li & Lu Lin, 2011. "Empirical likelihood inference for semi-parametric varying-coefficient partially linear EV models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 171-185, March.
    16. Cho, Hyunkeun & Kim, Seonjin, 2017. "Model specification test in a semiparametric regression model for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 105-116.
    17. Li, Daoji & Pan, Jianxin, 2013. "Empirical likelihood for generalized linear models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 63-73.
    18. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    19. Huang, Zhensheng & Zhou, Zhangong & Jiang, Rong & Qian, Weimin & Zhang, Riquan, 2010. "Empirical likelihood based inference for semiparametric varying coefficient partially linear models with error-prone linear covariates," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 497-504, March.
    20. Li, Yujie & Li, Gaorong & Lian, Heng & Tong, Tiejun, 2017. "Profile forward regression screening for ultra-high dimensional semiparametric varying coefficient partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 133-150.
    21. Li, Gaorong & Zhu, Lixing & Xue, Liugen & Feng, Sanying, 2010. "Empirical likelihood inference in partially linear single-index models for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 718-732, March.
    22. Zhao, Yan-Yong & Lin, Jin-Guan & Xu, Pei-Rong & Ye, Xu-Guo, 2015. "Orthogonality-projection-based estimation for semi-varying coefficient models with heteroscedastic errors," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 204-221.
    23. Zhao, Yan-Yong & Lin, Jin-Guan, 2019. "Estimation and test of jump discontinuities in varying coefficient models with empirical applications," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 145-163.
    24. Montezuma Dumangane & Nicoletta Rosati & Anna Volossovitch, 2009. "Departure from independence and stationarity in a handball match," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(7), pages 723-741.
    25. Wang, Kangning & Li, Shaomin & Sun, Xiaofei & Lin, Lu, 2019. "Modal regression statistical inference for longitudinal data semivarying coefficient models: Generalized estimating equations, empirical likelihood and variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 257-276.
    26. Huang, Zhensheng & Lin, Bingqing & Feng, Fan & Pang, Zhen, 2013. "Efficient penalized estimating method in the partially varying-coefficient single-index model," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 189-200.
    27. Huang, Zhensheng & Zhang, Riquan, 2009. "Empirical likelihood for nonparametric parts in semiparametric varying-coefficient partially linear models," Statistics & Probability Letters, Elsevier, vol. 79(16), pages 1798-1808, August.
    28. Tian, Ruiqin & Xue, Liugen & Liu, Chunling, 2014. "Penalized quadratic inference functions for semiparametric varying coefficient partially linear models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 94-110.
    29. Hou, Zhaohan & Wang, Lei, 2024. "Heterogeneous quantile regression for longitudinal data with subgroup structures," Computational Statistics & Data Analysis, Elsevier, vol. 194(C).
    30. Tang Qingguo & Cheng Longsheng, 2012. "Componentwise B-spline estimation for varying coefficient models with longitudinal data," Statistical Papers, Springer, vol. 53(3), pages 629-652, August.
    31. Bindele, Huybrechts F. & Abebe, Ash, 2015. "Semi-parametric rank regression with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 117-132.
    32. Yun Fang & Li-Xing Zhu, 2012. "Asymptotics of SIMEX-based variance estimation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(3), pages 329-345, April.
    33. Liugen Xue, 2009. "Empirical Likelihood Confidence Intervals for Response Mean with Data Missing at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 671-685, December.
    34. Xue, Liugen, 2009. "Empirical likelihood for linear models with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1353-1366, August.
    35. Jing Lv & Chaohui Guo & Jibo Wu, 2019. "Smoothed empirical likelihood inference via the modified Cholesky decomposition for quantile varying coefficient models with longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 999-1032, September.
    36. Xuemei Hu & Xiaohui Liu, 2013. "Empirical likelihood confidence regions for semi-varying coefficient models with linear process errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(1), pages 161-180, March.
    37. Zhang, Yuexia & Qin, Guoyou & Zhu, Zhongyi & Zhang, Jiajia, 2022. "Empirical likelihood inference for longitudinal data with covariate measurement errors: An application to the LEAN study," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    38. Zhang, Weiwei & Li, Gaorong & Xue, Liugen, 2011. "Profile inference on partially linear varying-coefficient errors-in-variables models under restricted condition," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3027-3040, November.
    39. Peixin Zhao & Liugen Xue, 2011. "Variable selection for varying coefficient models with measurement errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(2), pages 231-245, September.
    40. Zhao, Yan-Yong & Lin, Jin-Guan & Huang, Xing-Fang & Wang, Hong-Xia, 2016. "Adaptive jump-preserving estimates in varying-coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 65-80.
    41. Tang Qingguo & Cheng Longsheng, 2008. "M-estimation and B-spline approximation for varying coefficient models with longitudinal data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(7), pages 611-625.
    42. Li, Gaorong & Lin, Lu & Zhu, Lixing, 2012. "Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 85-111.
    43. Yang, Hu & Li, Tingting, 2010. "Empirical likelihood for semiparametric varying coefficient partially linear models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 80(2), pages 111-121, January.
    44. Huang, Zhensheng & Pang, Zhen & Lin, Bingqing & Shao, Quanxi, 2014. "Model structure selection in single-index-coefficient regression models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 159-175.
    45. Huang, Zhensheng & Zhang, Riquan, 2011. "Efficient empirical-likelihood-based inferences for the single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(5), pages 937-947, May.
    46. Yang, Yiping & Li, Gaorong & Peng, Heng, 2014. "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 1-18.
    47. Wang, Qihua & Xue, Liugen, 2011. "Statistical inference in partially-varying-coefficient single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 1-19, January.
    48. Feng, Sanying & Lian, Heng & Zhu, Fukang, 2016. "Reduced rank regression with possibly non-smooth criterion functions: An empirical likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 139-150.
    49. Feng, Sanying & He, Wenqi & Li, Feng, 2020. "Model detection and estimation for varying coefficient panel data models with fixed effects," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    50. Ke, Baofang & Zhao, Weihua & Wang, Lei, 2023. "Smoothed tensor quantile regression estimation for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    51. Xue, Liugen & Xue, Dong, 2011. "Empirical likelihood for semiparametric regression model with missing response data," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 723-740, April.
    52. Peixin Zhao & Liugen Xue, 2012. "Variable selection in semiparametric regression analysis for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 213-231, February.
    53. Zhao, Peixin & Xue, Liugen, 2010. "Variable selection for semiparametric varying coefficient partially linear errors-in-variables models," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1872-1883, September.

  51. Stute, Winfried & Xue, Liugen & Zhu, Lixing, 2007. "Empirical Likelihood Inference in Nonlinear Errors-in-Covariables Models With Validation Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 332-346, March.

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    1. Wang, Qihua & Lai, Peng, 2011. "Empirical likelihood calibration estimation for the median treatment difference in observational studies," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1596-1609, April.
    2. Liugen Xue, 2010. "Empirical Likelihood Local Polynomial Regression Analysis of Clustered Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 644-663, December.
    3. Zheng, Ming & Yu, Wen, 2011. "An empirical likelihood approach to data analysis under two-stage sampling designs," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 947-956, August.
    4. Qiang Chen & Lu Lin & Lixing Zhu, 2010. "Bias-corrected smoothed score function for single-index models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(1), pages 45-58, January.
    5. Wei Yu & Cuizhen Niu & Wangli Xu, 2014. "An empirical likelihood inference for the coefficient difference of a two-sample linear model with missing response data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(5), pages 675-693, July.
    6. Xiuli Wang & Gaorong Li & Lu Lin, 2011. "Empirical likelihood inference for semi-parametric varying-coefficient partially linear EV models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 171-185, March.
    7. Zhao, Yichuan & Chen, Feiming, 2008. "Empirical likelihood inference for censored median regression model via nonparametric kernel estimation," Journal of Multivariate Analysis, Elsevier, vol. 99(2), pages 215-231, February.
    8. Biao Zhang, 2016. "Empirical Likelihood in Causal Inference," Econometric Reviews, Taylor & Francis Journals, vol. 35(2), pages 201-231, February.
    9. Liugen Xue, 2009. "Empirical Likelihood Confidence Intervals for Response Mean with Data Missing at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 671-685, December.
    10. Xue, Liugen, 2009. "Empirical likelihood for linear models with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1353-1366, August.
    11. Wangli Xu & Lixing Zhu, 2015. "Nonparametric check for partial linear errors-in-covariables models with validation data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(4), pages 793-815, August.
    12. Xie Yanmei & Zhang Biao, 2017. "Empirical Likelihood in Nonignorable Covariate-Missing Data Problems," The International Journal of Biostatistics, De Gruyter, vol. 13(1), pages 1-20, May.
    13. Yang, Yiping & Li, Gaorong & Peng, Heng, 2014. "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 1-18.
    14. Sima Sharghi & Kevin Stoll & Wei Ning, 2024. "Statistical inferences for missing response problems based on modified empirical likelihood," Statistical Papers, Springer, vol. 65(7), pages 4079-4120, September.

  52. Lixing Zhu & Liugen Xue, 2006. "Empirical likelihood confidence regions in a partially linear single‐index model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 549-570, June.

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    1. Zhang, Jun & Gai, Yujie & Wu, Ping, 2013. "Estimation in linear regression models with measurement errors subject to single-indexed distortion," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 103-120.
    2. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," Papers 2108.04852, arXiv.org, revised Aug 2024.
    3. Jianhong Shi & Qian Yang & Xiongya Li & Weixing Song, 2017. "Effects of measurement error on a class of single-index varying coefficient regression models," Computational Statistics, Springer, vol. 32(3), pages 977-1001, September.
    4. Wong, Heung & Zhang, Riquan & Leung, Bartholomew & Huang, Zhensheng, 2013. "Testing the significance of index parameters in varying-coefficient single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 297-308.
    5. Zhensheng Huang & Xing Sun & Riquan Zhang, 2022. "Estimation for partially varying-coefficient single-index models with distorted measurement errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(2), pages 175-201, February.
    6. Liugen Xue, 2010. "Empirical Likelihood Local Polynomial Regression Analysis of Clustered Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 644-663, December.
    7. Huang, Zhensheng & Pang, Zhen & Zhang, Riquan, 2013. "Adaptive profile-empirical-likelihood inferences for generalized single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 70-82.
    8. Huang, Zhensheng, 2012. "Efficient inferences on the varying-coefficient single-index model with empirical likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4413-4420.
    9. Lai, Peng & Zhang, Qingzhao & Lian, Heng & Wang, Qihua, 2016. "Efficient estimation for the heteroscedastic single-index varying coefficient models," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 84-93.
    10. Lai, Peng & Wang, Qihua, 2014. "Semiparametric efficient estimation for partially linear single-index models with responses missing at random," Journal of Multivariate Analysis, Elsevier, vol. 128(C), pages 33-50.
    11. Wanrong Liu & Xuewen Lu, 2011. "Empirical likelihood for density-weighted average derivatives," Statistical Papers, Springer, vol. 52(2), pages 391-412, May.
    12. Feng, Sanying & Kong, Kaidi & Kong, Yinfei & Li, Gaorong & Wang, Zhaoliang, 2022. "Statistical inference of heterogeneous treatment effect based on single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    13. Xuemin Zi & Changliang Zou & Yukun Liu, 2012. "Two-sample empirical likelihood method for difference between coefficients in linear regression model," Statistical Papers, Springer, vol. 53(1), pages 83-93, February.
    14. Ke Wang & Dehui Wang, 2024. "Estimation for partially linear single-index spatial autoregressive model with covariate measurement errors," Statistical Papers, Springer, vol. 65(7), pages 4201-4241, September.
    15. Yang, Hu & Guo, Chaohui & Lv, Jing, 2014. "A robust and efficient estimation method for single-index varying-coefficient models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 119-127.
    16. Huang, Zhensheng & Pang, Zhen, 2012. "Corrected empirical likelihood inference for right-censored partially linear single-index model," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 276-284.
    17. Kangning Wang & Lu Lin, 2017. "Robust and efficient direction identification for groupwise additive multiple-index models and its applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 22-45, March.
    18. Strzalkowska-Kominiak, Ewa & Cao, Ricardo, 2013. "Maximum likelihood estimation for conditional distribution single-index models under censoring," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 74-98.
    19. Huang, Zhensheng & Pang, Zhen & Hu, Tao, 2013. "Testing structural change in partially linear single-index models with error-prone linear covariates," Computational Statistics & Data Analysis, Elsevier, vol. 59(C), pages 121-133.
    20. Li, Gaorong & Feng, Sanying & Peng, Heng, 2011. "A profile-type smoothed score function for a varying coefficient partially linear model," Journal of Multivariate Analysis, Elsevier, vol. 102(2), pages 372-385, February.
    21. Ruidong Han & Xinghui Wang & Shuhe Hu, 2018. "Asymptotics of the weighted least squares estimation for AR(1) processes with applications to confidence intervals," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 479-490, August.
    22. Zhang, Hong-Fan, 2021. "Iterative GMM for partially linear single-index models with partly endogenous regressors," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    23. Zhensheng Huang, 2011. "Empirical likelihood for generalized partially linear varying-coefficient models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(6), pages 1265-1275, May.
    24. Zhang, Junhua & Feng, Sanying & Li, Gaorong & Lian, Heng, 2011. "Empirical likelihood inference for partially linear panel data models with fixed effects," Economics Letters, Elsevier, vol. 113(2), pages 165-167.
    25. Wu, Jingwei & Peng, Hanxiang & Tu, Wanzhu, 2019. "Large-sample estimation and inference in multivariate single-index models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 382-396.
    26. Hongxia Wang & Zihan Zhao & Hongxia Hao & Chao Huang, 2023. "Estimation and Inference for Spatio-Temporal Single-Index Models," Mathematics, MDPI, vol. 11(20), pages 1-32, October.
    27. Li, Gao-Rong & Zhu, Li-Ping & Zhu, Li-Xing, 2010. "Adaptive confidence region for the direction in semiparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1364-1377, July.
    28. Xu, Peirong & Zhu, Lixing, 2012. "Estimation for a marginal generalized single-index longitudinal model," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 285-299.
    29. Kai Yang & Xue Ding & Xiaohui Yuan, 2022. "Bayesian empirical likelihood inference and order shrinkage for autoregressive models," Statistical Papers, Springer, vol. 63(1), pages 97-121, February.
    30. Qiang Chen & Lu Lin & Lixing Zhu, 2010. "Bias-corrected smoothed score function for single-index models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(1), pages 45-58, January.
    31. Zhang, Jun & Zhu, Li-Xing & Liang, Hua, 2012. "Nonlinear models with measurement errors subject to single-indexed distortion," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 1-23.
    32. Wei Yu & Cuizhen Niu & Wangli Xu, 2014. "An empirical likelihood inference for the coefficient difference of a two-sample linear model with missing response data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(5), pages 675-693, July.
    33. Zhensheng Huang, 2011. "Statistical estimation in partially linear single-index models with error-prone linear covariates," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(2), pages 339-350.
    34. Zhensheng Huang, 2012. "Empirical likelihood for varying-coefficient single-index model with right-censored data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(1), pages 55-71, January.
    35. Lai, Peng & Wang, Qihua & Zhou, Xiao-Hua, 2014. "Variable selection and semiparametric efficient estimation for the heteroscedastic partially linear single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 241-256.
    36. Lai, Peng & Wang, Qihua & Lian, Heng, 2012. "Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 422-432.
    37. Matsushita, Yukitoshi & Otsu, Taisuke, 2018. "Likelihood inference on semiparametric models: average derivative and treatment effect," LSE Research Online Documents on Economics 85870, London School of Economics and Political Science, LSE Library.
    38. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    39. Lai, Peng & Li, Gaorong & Lian, Heng, 2013. "Semiparametric estimation of fixed effects panel data single-index model," Statistics & Probability Letters, Elsevier, vol. 83(6), pages 1595-1602.
    40. Li, Gaorong & Zhu, Lixing & Xue, Liugen & Feng, Sanying, 2010. "Empirical likelihood inference in partially linear single-index models for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 718-732, March.
    41. Lai, Peng & Li, Gaorong & Lian, Heng, 2013. "Quadratic inference functions for partially linear single-index models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 118(C), pages 115-127.
    42. Hiroaki Ogata & Masanobu Taniguchi, 2009. "Cressie–Read Power‐Divergence Statistics for Non‐Gaussian Vector Stationary Processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(1), pages 141-156, March.
    43. Ewa Strzalkowska-Kominiak & Ricardo Cao, 2014. "Beran-based approach for single-index models under censoring," Computational Statistics, Springer, vol. 29(5), pages 1243-1261, October.
    44. Xue, Liugen, 2024. "Empirical likelihood in a partially linear single-index model with censored response data," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
    45. Zhiyong Chen & Jianbao Chen, 2022. "Bayesian analysis of partially linear, single-index, spatial autoregressive models," Computational Statistics, Springer, vol. 37(1), pages 327-353, March.
    46. Matsushita, Yukitoshi & Otsu, Taisuke, 2020. "Likelihood inference on semiparametric models with generated regressors," LSE Research Online Documents on Economics 102696, London School of Economics and Political Science, LSE Library.
    47. Xinyuan Dong & Yingye Zheng & Daniel W. Lin & Lisa Newcomb & Ying‐Qi Zhao, 2023. "Constructing time‐invariant dynamic surveillance rules for optimal monitoring schedules," Biometrics, The International Biometric Society, vol. 79(4), pages 3895-3906, December.
    48. Huang, Zhensheng & Lin, Bingqing & Feng, Fan & Pang, Zhen, 2013. "Efficient penalized estimating method in the partially varying-coefficient single-index model," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 189-200.
    49. Wai-Yin Poon & Hai-Bin Wang, 2014. "Multivariate partially linear single-index models: Bayesian analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 755-768, December.
    50. Hua Liang & Yongsong Qin & Xinyu Zhang & David Ruppert, 2009. "Empirical Likelihood‐Based Inferences for Generalized Partially Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 433-443, September.
    51. Yang, Suigen & Xue, Liugen & Li, Gaorong, 2014. "Simultaneous confidence band for single-index random effects models with longitudinal data," Statistics & Probability Letters, Elsevier, vol. 85(C), pages 6-14.
    52. Yukitoshi Matsushita & Taisuke Otsu, 2018. "Likelihood Inference on Semiparametric Models: Average Derivative and Treatment Effect," The Japanese Economic Review, Springer, vol. 69(2), pages 133-155, June.
    53. Peirong Xu & Jun Zhang & Xingfang Huang & Tao Wang, 2016. "Efficient estimation for marginal generalized partially linear single-index models with longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 413-431, September.
    54. D. Wang & C. S. McMahan & C. M. Gallagher & K. B. Kulasekera, 2014. "Semiparametric group testing regression models," Biometrika, Biometrika Trust, vol. 101(3), pages 587-598.
    55. Gueuning, Thomas & Claeskens, Gerda, 2016. "Confidence intervals for high-dimensional partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 13-29.
    56. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    57. Guo-Liang Fan & Han-Ying Liang & Zhen-Sheng Huang, 2012. "Empirical likelihood for partially time-varying coefficient models with dependent observations," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(1), pages 71-84.
    58. Cui, Xia & Härdle, Wolfgang Karl & Zhu, Lixing, 2009. "Generalized single-index models: The EFM approach," SFB 649 Discussion Papers 2009-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    59. Chang, Ziqing & Xue, Liugen & Zhu, Lixing, 2010. "On an asymptotically more efficient estimation of the single-index model," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1898-1901, September.
    60. Liugen Xue, 2009. "Empirical Likelihood Confidence Intervals for Response Mean with Data Missing at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 671-685, December.
    61. Yukitoshi Matsushita & Taisuke Otsu, 2019. "Jackknife, small bandwidth and high-dimensional asymptotics," STICERD - Econometrics Paper Series 605, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    62. Lu, Xuewen, 2009. "Empirical likelihood for heteroscedastic partially linear models," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 387-396, March.
    63. Yukitoshi Matsushita & Taisuke Otsu, 2016. "Likelihood inference on semiparametric models with generated regressors," STICERD - Econometrics Paper Series 587, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    64. Xue, Liugen & Zhang, Jinghua, 2020. "Empirical likelihood for partially linear single-index models with missing observations," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    65. Xue, Liugen, 2009. "Empirical likelihood for linear models with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1353-1366, August.
    66. Huang, Zhensheng, 2012. "Empirical likelihood for the parametric part in partially linear errors-in-function models," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 63-66.
    67. Jianglin Fang & Wanrong Liu & Xuewen Lu, 2018. "Empirical likelihood for heteroscedastic partially linear single-index models with growing dimensional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(3), pages 255-281, April.
    68. Harold D Chiang & Yukitoshi Matsushita & Taisuke Otsu, 2021. "Multiway empirical likelihood," STICERD - Econometrics Paper Series 617, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    69. Yunan Wu & Lan Wang, 2021. "Resampling‐based confidence intervals for model‐free robust inference on optimal treatment regimes," Biometrics, The International Biometric Society, vol. 77(2), pages 465-476, June.
    70. Li, Gaorong & Lin, Lu & Zhu, Lixing, 2012. "Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 85-111.
    71. Huang, Zhensheng & Pang, Zhen & Lin, Bingqing & Shao, Quanxi, 2014. "Model structure selection in single-index-coefficient regression models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 159-175.
    72. Zhu, Lixing & Lin, Lu & Cui, Xia & Li, Gaorong, 2010. "Bias-corrected empirical likelihood in a multi-link semiparametric model," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 850-868, April.
    73. Lexin Li & Liping Zhu & Lixing Zhu, 2011. "Inference on the primary parameter of interest with the aid of dimension reduction estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 59-80, January.
    74. Huang, Zhensheng & Zhang, Riquan, 2011. "Efficient empirical-likelihood-based inferences for the single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(5), pages 937-947, May.
    75. Yang, Yiping & Li, Gaorong & Peng, Heng, 2014. "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 1-18.
    76. Lu Lin & Lili Liu & Xia Cui & Kangning Wang, 2021. "A generalized semiparametric regression and its efficient estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 1-24, March.
    77. Wang, Qihua & Xue, Liugen, 2011. "Statistical inference in partially-varying-coefficient single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 1-19, January.
    78. Junmin Liu & Deli Zhu & Luoyao Yu & Xuehu Zhu, 2023. "Specification testing of partially linear single-index models: a groupwise dimension reduction-based adaptive-to-model approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 232-262, March.
    79. Feng, Sanying & Lian, Heng & Zhu, Fukang, 2016. "Reduced rank regression with possibly non-smooth criterion functions: An empirical likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 139-150.
    80. Liu, Xuejing & Yu, Zhou & Wen, Xuerong Meggie & Paige, Robert, 2015. "On testing common indices for two multi-index models: A link-free approach," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 75-85.
    81. Zhang, Yuexia & Qin, Guoyou & Zhu, Zhongyi & Zhang, Jiajia, 2018. "Robust estimation in linear regression models for longitudinal data with covariate measurement errors and outliers," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 261-275.
    82. Han, Zhong-Cheng & Lin, Jin-Guan & Zhao, Yan-Yong, 2020. "Adaptive semiparametric estimation for single index models with jumps," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    83. Guo, Xu & Xu, Wangli & Zhu, Lixing, 2014. "Multi-index regression models with missing covariates at random," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 345-363.
    84. Yiping Yang & Tiejun Tong & Gaorong Li, 2019. "SIMEX estimation for single-index model with covariate measurement error," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 137-161, March.
    85. Xue, Liugen & Xue, Dong, 2011. "Empirical likelihood for semiparametric regression model with missing response data," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 723-740, April.
    86. Zhao, Weihua & Zhang, Riquan & Huang, Zhensheng & Feng, Jingyan, 2012. "Partially linear single-index beta regression model and score test," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 116-123, January.
    87. Wang, Taining & Henderson, Daniel J., 2022. "Estimation of a varying coefficient, fixed-effects Cobb–Douglas production function in levels," Economics Letters, Elsevier, vol. 213(C).
    88. Chaohua Dong & Jiti Gao & Dag Tjostheim, 2014. "Estimation for Single-index and Partially Linear Single-index Nonstationary Time Series Models," Monash Econometrics and Business Statistics Working Papers 7/14, Monash University, Department of Econometrics and Business Statistics.

  53. Zhu, Lixing & Miao, Baiqi & Peng, Heng, 2006. "On Sliced Inverse Regression With High-Dimensional Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 630-643, June.

    Cited by:

    1. Zhang, Xin & Wang, Chong & Wu, Yichao, 2018. "Functional envelope for model-free sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 37-50.
    2. Guochang Wang & Beiting Liang & Hansheng Wang & Baoxue Zhang & Baojian Xie, 2021. "Dimension reduction for functional regression with a binary response," Statistical Papers, Springer, vol. 62(1), pages 193-208, February.
    3. Coudret, R. & Girard, S. & Saracco, J., 2014. "A new sliced inverse regression method for multivariate response," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 285-299.
    4. Ming-Yueh Huang & Chin-Tsang Chiang, 2017. "An Effective Semiparametric Estimation Approach for the Sufficient Dimension Reduction Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1296-1310, July.
    5. Zhu, Li-Ping & Zhu, Li-Xing, 2007. "On kernel method for sliced average variance estimation," Journal of Multivariate Analysis, Elsevier, vol. 98(5), pages 970-991, May.
    6. Zeng, Bilin & Yu, Zhou & Wen, Xuerong Meggie, 2015. "A note on cumulative mean estimation," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 322-327.
    7. Guochang Wang, 2017. "Dimension reduction in functional regression with categorical predictor," Computational Statistics, Springer, vol. 32(2), pages 585-609, June.
    8. Xiaobing Zhao & Xian Zhou, 2020. "Partial sufficient dimension reduction on additive rates model for recurrent event data with high-dimensional covariates," Statistical Papers, Springer, vol. 61(2), pages 523-541, April.
    9. Seung Jun Shin & Yichao Wu & Hao Helen Zhang & Yufeng Liu, 2014. "Probability-enhanced sufficient dimension reduction for binary classification," Biometrics, The International Biometric Society, vol. 70(3), pages 546-555, September.
    10. Takuma Yoshida, 2017. "Nonlinear surface regression with dimension reduction method," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(1), pages 29-50, January.
    11. Zhao, Wenbiao & Zhu, Xuehu & Zhu, Lixing, 2025. "Minimax rates of convergence for sliced inverse regression with differential privacy," Computational Statistics & Data Analysis, Elsevier, vol. 201(C).
    12. Chen, Canyi & Xu, Wangli & Zhu, Liping, 2022. "Distributed estimation in heterogeneous reduced rank regression: With application to order determination in sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    13. Deng, Jianqiu & Yang, Xiaojie & Wang, Qihua, 2022. "Surrogate space based dimension reduction for nonignorable nonresponse," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    14. Xiao, Zhen & Zhang, Qi, 2022. "Dimension reduction for block-missing data based on sparse sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    15. Zhenghui Feng & Lu Lin & Ruoqing Zhu & Lixing Zhu, 2020. "Nonparametric variable selection and its application to additive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 827-854, June.
    16. Feng, Zheng-Hui & Lin, Lu & Zhu, Ruo-Qing & Zhu, Li-Xing, 2018. "Nonparametric Variable Selection and Its Application to Additive Models," IRTG 1792 Discussion Papers 2018-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Scrucca, Luca, 2011. "Model-based SIR for dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3010-3026, November.
    18. Sadikoglu, Serhan, 2019. "Essays in econometric theory," Other publications TiSEM 99d83644-f9dc-49e3-a4e1-5, Tilburg University, School of Economics and Management.
    19. Qin Wang & Yuan Xue, 2023. "A structured covariance ensemble for sufficient dimension reduction," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(3), pages 777-800, September.
    20. Hilafu, Haileab & Yin, Xiangrong, 2013. "Sufficient dimension reduction in multivariate regressions with categorical predictors," Computational Statistics & Data Analysis, Elsevier, vol. 63(C), pages 139-147.
    21. Girard, Stéphane & Lorenzo, Hadrien & Saracco, Jérôme, 2022. "Advanced topics in Sliced Inverse Regression," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    22. Chuanlong Xie & Lixing Zhu, 2018. "A minimum projected-distance test for parametric single-index Berkson models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 700-715, September.
    23. Huiwen Wang & Zhichao Wang & Shanshan Wang, 2021. "Sliced inverse regression method for multivariate compositional data modeling," Statistical Papers, Springer, vol. 62(1), pages 361-393, February.
    24. Wang, Guochang & Lin, Nan & Zhang, Baoxue, 2013. "Functional contour regression," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 1-13.
    25. Wang, Qin & Xue, Yuan, 2021. "An ensemble of inverse moment estimators for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    26. Yuan Xue & Xiangrong Yin, 2015. "Sufficient dimension folding for a functional of conditional distribution of matrix- or array-valued objects," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(2), pages 253-269, June.
    27. Fan, Guo-Liang & Xu, Hong-Xia & Liang, Han-Ying, 2019. "Dimension reduction estimation for central mean subspace with missing multivariate response," Journal of Multivariate Analysis, Elsevier, vol. 174(C).
    28. Zifang Guo & Lexin Li & Wenbin Lu & Bing Li, 2015. "Groupwise Dimension Reduction via Envelope Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1515-1527, December.
    29. Lili Xia & Tingyu Lai & Zhongzhan Zhang, 2023. "An Adaptive-to-Model Test for Parametric Functional Single-Index Model," Mathematics, MDPI, vol. 11(8), pages 1-25, April.
    30. Zhenghui Feng & Xuerong Meggie Wen & Zhou Yu & Lixing Zhu, 2013. "On Partial Sufficient Dimension Reduction With Applications to Partially Linear Multi-Index Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 237-246, March.
    31. Kim, Kyongwon, 2022. "On principal graphical models with application to gene network," Computational Statistics & Data Analysis, Elsevier, vol. 166(C).
    32. Pircalabelu, Eugen & Artemiou, Andreas, 2021. "Graph informed sliced inverse regression," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    33. Fan, Jianqing & Xue, Lingzhou & Yao, Jiawei, 2017. "Sufficient forecasting using factor models," Journal of Econometrics, Elsevier, vol. 201(2), pages 292-306.
    34. Xie, Chuanlong & Zhu, Lixing, 2019. "A goodness-of-fit test for variable-adjusted models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 27-48.
    35. Niwen Zhou & Xu Guo & Lixing Zhu, 2022. "The role of propensity score structure in asymptotic efficiency of estimated conditional quantile treatment effect," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 718-743, June.
    36. Chen, Fei & Shi, Lei & Zhu, Xuehu & Zhu, Lixing, 2018. "Generalized principal Hessian directions for mixture multivariate skew elliptical distributions," Journal of Multivariate Analysis, Elsevier, vol. 168(C), pages 142-159.
    37. Feng, Zhenghui & Zhu, Lixing, 2012. "An alternating determination–optimization approach for an additive multi-index model," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1981-1993.
    38. Zhu, Xuehu & Guo, Xu & Wang, Tao & Zhu, Lixing, 2020. "Dimensionality determination: A thresholding double ridge ratio approach," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).
    39. Benoît Liquet & Jérôme Saracco, 2012. "A graphical tool for selecting the number of slices and the dimension of the model in SIR and SAVE approaches," Computational Statistics, Springer, vol. 27(1), pages 103-125, March.
    40. Nordhausen, Klaus & Oja, Hannu & Tyler, David E., 2022. "Asymptotic and bootstrap tests for subspace dimension," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    41. Fang, Fang & Yu, Zhou, 2020. "Model averaging assisted sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    42. Dong, Yuexiao & Yu, Zhou & Zhu, Liping, 2015. "Robust inverse regression for dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 71-81.
    43. Chiancone, Alessandro & Forbes, Florence & Girard, Stéphane, 2017. "Student Sliced Inverse Regression," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 441-456.
    44. Zeng, Yicheng & Zhu, Lixing, 2023. "Order determination for spiked-type models with a divergent number of spikes," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    45. Xie, Chuanlong & Zhu, Lixing, 2020. "Generalized kernel-based inverse regression methods for sufficient dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    46. Wang, Qin & Yin, Xiangrong, 2011. "Estimation of inverse mean: An orthogonal series approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1656-1664, April.
    47. Wang, Qin & Yin, Xiangrong, 2008. "A nonlinear multi-dimensional variable selection method for high dimensional data: Sparse MAVE," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4512-4520, May.
    48. Shih‐Hao Huang & Kerby Shedden & Hsin‐wen Chang, 2023. "Inference for the dimension of a regression relationship using pseudo‐covariates," Biometrics, The International Biometric Society, vol. 79(3), pages 2394-2403, September.
    49. Wei Luo, 2022. "On efficient dimension reduction with respect to the interaction between two response variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 269-294, April.
    50. Zhu, Liping & Zhong, Wei, 2015. "Estimation and inference on central mean subspace for multivariate response data," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 68-83.
    51. Zhou, Jingke & Xu, Wangli & Zhu, Lixing, 2015. "Robust estimating equation-based sufficient dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 99-118.
    52. Lexin Li & Xiangrong Yin, 2008. "Sliced Inverse Regression with Regularizations," Biometrics, The International Biometric Society, vol. 64(1), pages 124-131, March.

  54. Xue, Liu-Gen & Zhu, Lixing, 2006. "Empirical likelihood for single-index models," Journal of Multivariate Analysis, Elsevier, vol. 97(6), pages 1295-1312, July.

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    1. Jianhong Shi & Qian Yang & Xiongya Li & Weixing Song, 2017. "Effects of measurement error on a class of single-index varying coefficient regression models," Computational Statistics, Springer, vol. 32(3), pages 977-1001, September.
    2. Gong, Yun & Peng, Liang & Qi, Yongcheng, 2010. "Smoothed jackknife empirical likelihood method for ROC curve," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1520-1531, July.
    3. Liugen Xue, 2010. "Empirical Likelihood Local Polynomial Regression Analysis of Clustered Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(4), pages 644-663, December.
    4. Huang, Zhensheng & Pang, Zhen & Zhang, Riquan, 2013. "Adaptive profile-empirical-likelihood inferences for generalized single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 62(C), pages 70-82.
    5. Bravo, Francesco, 2009. "Two-step generalised empirical likelihood inference for semiparametric models," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1412-1431, August.
    6. Qihua Wang & Tao Zhang & Wolfgang Karl Härdle, 2016. "An Extended Single-index Model with Missing Response at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1140-1152, December.
    7. Yang, Hu & Guo, Chaohui & Lv, Jing, 2014. "A robust and efficient estimation method for single-index varying-coefficient models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 119-127.
    8. Wu, Jingwei & Peng, Hanxiang & Tu, Wanzhu, 2019. "Large-sample estimation and inference in multivariate single-index models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 382-396.
    9. Hongxia Wang & Zihan Zhao & Hongxia Hao & Chao Huang, 2023. "Estimation and Inference for Spatio-Temporal Single-Index Models," Mathematics, MDPI, vol. 11(20), pages 1-32, October.
    10. Zaichao Du & Juan Carlos Escanciano, 2015. "A Nonparametric Distribution-Free Test for Serial Independence of Errors," Econometric Reviews, Taylor & Francis Journals, vol. 34(6-10), pages 1011-1034, December.
    11. Francesco Bravo & David Jacho-Chavez, 2011. "Empirical Likelihood for Efficient Semiparametric Average Treatment Effects," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 1-24.
    12. Li, Gao-Rong & Zhu, Li-Ping & Zhu, Li-Xing, 2010. "Adaptive confidence region for the direction in semiparametric regressions," Journal of Multivariate Analysis, Elsevier, vol. 101(6), pages 1364-1377, July.
    13. Xiuli Wang & Gaorong Li & Lu Lin, 2011. "Empirical likelihood inference for semi-parametric varying-coefficient partially linear EV models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 171-185, March.
    14. Pang, Zhen & Xue, Liugen, 2012. "Estimation for the single-index models with random effects," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1837-1853.
    15. Tang, Xingyu & Li, Jianbo & Lian, Heng, 2013. "Empirical likelihood for partially linear proportional hazards models with growing dimensions," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 22-32.
    16. Li, Gaorong & Zhu, Lixing & Xue, Liugen & Feng, Sanying, 2010. "Empirical likelihood inference in partially linear single-index models for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 718-732, March.
    17. Peirong Xu & Jun Zhang & Xingfang Huang & Tao Wang, 2016. "Efficient estimation for marginal generalized partially linear single-index models with longitudinal data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 413-431, September.
    18. Bindele, Huybrechts F. & Abebe, Ash, 2015. "Semi-parametric rank regression with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 117-132.
    19. Claudio Agostinelli & Ana M. Bianco & Graciela Boente, 2020. "Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 855-893, June.
    20. Bravo, Francesco & Escanciano, Juan Carlos & Van Keilegom, Ingrid, 2015. "Wilks' Phenomenon in Two-Step Semiparametric Empirical Likelihood Inference," LIDAM Discussion Papers ISBA 2015016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    21. Chang, Ziqing & Xue, Liugen & Zhu, Lixing, 2010. "On an asymptotically more efficient estimation of the single-index model," Journal of Multivariate Analysis, Elsevier, vol. 101(8), pages 1898-1901, September.
    22. Hu, Xuemei & Wang, Zhizhong & Zhao, Zhiwei, 2009. "Empirical likelihood for semiparametric varying-coefficient partially linear errors-in-variables models," Statistics & Probability Letters, Elsevier, vol. 79(8), pages 1044-1052, April.
    23. Liugen Xue, 2009. "Empirical Likelihood Confidence Intervals for Response Mean with Data Missing at Random," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(4), pages 671-685, December.
    24. Maina, Florence Wanjiru & Mburu, John & Gitau, George Karuoya & Van Leeuwen, John, 2018. "Assessing The Economic Efficiency Of Milk Production Among Small-Scale Dairy Farmers In Mukurweini Sub-County, Nyeri County, Kenya," Dissertations and Theses 280032, University of Nairobi, Department of Agricultural Economics.
    25. Xue, Liugen, 2009. "Empirical likelihood for linear models with missing responses," Journal of Multivariate Analysis, Elsevier, vol. 100(7), pages 1353-1366, August.
    26. Jianglin Fang & Wanrong Liu & Xuewen Lu, 2018. "Empirical likelihood for heteroscedastic partially linear single-index models with growing dimensional data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 81(3), pages 255-281, April.
    27. Song Chen & Ingrid Van Keilegom, 2009. "A review on empirical likelihood methods for regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(3), pages 415-447, November.
    28. Otsu, Taisuke & Takahata, Keisuke & Xu, Mengshan, 2023. "Empirical likelihood inference for monotone index model," LSE Research Online Documents on Economics 118123, London School of Economics and Political Science, LSE Library.
    29. Li, Gaorong & Lin, Lu & Zhu, Lixing, 2012. "Empirical likelihood for a varying coefficient partially linear model with diverging number of parameters," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 85-111.
    30. Huang, Zhensheng & Pang, Zhen & Lin, Bingqing & Shao, Quanxi, 2014. "Model structure selection in single-index-coefficient regression models," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 159-175.
    31. Huang, Zhensheng & Zhang, Riquan, 2011. "Efficient empirical-likelihood-based inferences for the single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(5), pages 937-947, May.
    32. Yang, Yiping & Li, Gaorong & Peng, Heng, 2014. "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 127(C), pages 1-18.
    33. Wang, Qihua & Xue, Liugen, 2011. "Statistical inference in partially-varying-coefficient single-index model," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 1-19, January.
    34. Liu, Xuejing & Yu, Zhou & Wen, Xuerong Meggie & Paige, Robert, 2015. "On testing common indices for two multi-index models: A link-free approach," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 75-85.
    35. Han, Zhong-Cheng & Lin, Jin-Guan & Zhao, Yan-Yong, 2020. "Adaptive semiparametric estimation for single index models with jumps," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    36. Guo, Xu & Xu, Wangli & Zhu, Lixing, 2014. "Multi-index regression models with missing covariates at random," Journal of Multivariate Analysis, Elsevier, vol. 123(C), pages 345-363.
    37. Yiping Yang & Tiejun Tong & Gaorong Li, 2019. "SIMEX estimation for single-index model with covariate measurement error," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 137-161, March.

  55. Henze, N. & Klar, B. & Zhu, L. X., 2005. "Checking the adequacy of the multivariate semiparametric location shift model," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 238-256, April.

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    1. Jiménez-Gamero, M.D. & Alba-Fernández, M.V. & Jodrá, P. & Barranco-Chamorro, I., 2017. "Fast tests for the two-sample problem based on the empirical characteristic function," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 137(C), pages 390-410.
    2. Chen, Feifei & Jiménez–Gamero, M. Dolores & Meintanis, Simos & Zhu, Lixing, 2022. "A general Monte Carlo method for multivariate goodness–of–fit testing applied to elliptical families," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    3. Juan Carlos Pardo-Fernández & María Dolores Jiménez-Gamero & Anouar El Ghouch, 2015. "A Non-parametric ANOVA-type Test for Regression Curves Based on Characteristic Functions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 197-213, March.
    4. Zdeněk Hlávka & Marie Hušková & Claudia Kirch & Simos G. Meintanis, 2017. "Fourier--type tests involving martingale difference processes," Econometric Reviews, Taylor & Francis Journals, vol. 36(4), pages 468-492, April.
    5. L. Baringhaus & B. Ebner & N. Henze, 2017. "The limit distribution of weighted $$L^2$$ L 2 -goodness-of-fit statistics under fixed alternatives, with applications," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(5), pages 969-995, October.
    6. M. D. Jiménez-Gamero & J. L. Moreno-Rebollo & J. A. Mayor-Gallego, 2018. "On the estimation of the characteristic function in finite populations with applications," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(1), pages 95-121, March.
    7. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2020. "Change-point methods for multivariate time-series: paired vectorial observations," Statistical Papers, Springer, vol. 61(4), pages 1351-1383, August.

  56. Lu Lin & Lixing Zhu & K. Yuen, 2005. "Profile empirical likelihood for parametric and semiparametric models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 485-505, September.

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    1. Lu Lin & Lili Liu & Xia Cui & Kangning Wang, 2021. "A generalized semiparametric regression and its efficient estimation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 1-24, March.

  57. Cui, Hengjian & Ng, Kai W. & Zhu, Lixing, 2004. "Estimation in mixed effects model with errors in variables," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 53-73, October.

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    1. Kheradmandi, Ameneh & Rasekh, Abdolrahman, 2015. "Estimation in skew-normal linear mixed measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 1-11.
    2. Lili Yue & Jianhong Shi & Jingxuan Luo & Jinguan Lin, 2023. "A Parametric Bootstrap Approach for a One-Way Error Component Regression Model with Measurement Errors," Mathematics, MDPI, vol. 11(19), pages 1-13, October.
    3. Joelmir A. Borssoi & Gilberto A. Paula & Manuel Galea, 2020. "Elliptical linear mixed models with a covariate subject to measurement error," Statistical Papers, Springer, vol. 61(1), pages 31-69, February.
    4. Xing-cai Zhou & Jin-Guan Lin, 2014. "Empirical likelihood for varying-coefficient semiparametric mixed-effects errors-in-variables models with longitudinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 51-69, March.
    5. Ping Wu & Li Xing Zhu, 2010. "An Orthogonality‐Based Estimation of Moments for Linear Mixed Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 253-263, June.
    6. Zaixing Li, 2013. "Two kinds of variance/covariance estimates in linear mixed models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(3), pages 303-324, April.
    7. Karim Zare & Abdolrahman Rasekh & Ali Rasekhi, 2012. "Estimation of variance components in linear mixed measurement error models," Statistical Papers, Springer, vol. 53(4), pages 849-863, November.
    8. Wangli Xu & Lixing Zhu, 2009. "Kernel‐based Generalized Cross‐validation in Non‐parametric Mixed‐effect Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 229-247, June.

  58. He X. & Zhu L-X., 2003. "A Lack-of-Fit Test for Quantile Regression," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1013-1022, January.

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    1. Tang, Yanlin & Song, Xinyuan & Zhu, Zhongyi, 2015. "Threshold effect test in censored quantile regression," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 149-156.
    2. Conde-Amboage, Mercedes & Sánchez-Sellero, César & González-Manteiga, Wenceslao, 2015. "A lack-of-fit test for quantile regression models with high-dimensional covariates," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 128-138.
    3. Hao, Meiling & Lin, Yuanyuan & Shen, Guohao & Su, Wen, 2023. "Nonparametric inference on smoothed quantile regression process," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
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    17. Pasquale Dolce & Cristina Davino & Domenico Vistocco, 2022. "Quantile composite-based path modeling: algorithms, properties and applications," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 909-949, December.
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    Cited by:

    1. Yanqin Feng & Ling Ma & Jianguo Sun, 2015. "Regression Analysis of Current Status Data Under the Additive Hazards Model with Auxiliary Covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 118-136, March.
    2. Xiuli Wang & Gaorong Li & Lu Lin, 2011. "Empirical likelihood inference for semi-parametric varying-coefficient partially linear EV models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 171-185, March.
    3. Xing-cai Zhou & Jin-Guan Lin, 2014. "Empirical likelihood for varying-coefficient semiparametric mixed-effects errors-in-variables models with longitudinal data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(1), pages 51-69, March.
    4. Yan, Li & Chen, Xia, 2014. "Empirical likelihood for partly linear models with errors in all variables," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 275-288.
    5. Bang-Qiang He & Xing-Jian Hong & Guo-Liang Fan, 2020. "Penalized empirical likelihood for partially linear errors-in-variables panel data models with fixed effects," Statistical Papers, Springer, vol. 61(6), pages 2351-2381, December.
    6. You, Jinhong & Chen, Gemai, 2006. "Estimation of a semiparametric varying-coefficient partially linear errors-in-variables model," Journal of Multivariate Analysis, Elsevier, vol. 97(2), pages 324-341, February.
    7. Cui, Hengjian & Ng, Kai W. & Zhu, Lixing, 2004. "Estimation in mixed effects model with errors in variables," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 53-73, October.
    8. Bianco, Ana M. & Spano, Paula M., 2017. "Robust estimation in partially linear errors-in-variables models," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 46-64.
    9. Leise Kelli de Oliveira & Gracielle Gonçalves Ferreira de Araújo & Bruno Vieira Bertoncini & Carlos David Pedrosa & Francisco Gildemir Ferreira da Silva, 2022. "Modelling Freight Trip Generation Based on Deliveries for Brazilian Municipalities," Sustainability, MDPI, vol. 14(16), pages 1-18, August.
    10. Haibo Zhou & Jinhong You & Bin Zhou, 2010. "Statistical inference for fixed-effects partially linear regression models with errors in variables," Statistical Papers, Springer, vol. 51(3), pages 629-650, September.

  60. Sun, Liuquan & Zhu, Lixing, 2000. "A semiparametric model for truncated and censored data," Statistics & Probability Letters, Elsevier, vol. 48(3), pages 217-227, July.

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    1. Dikta, Gerhard, 2014. "Asymptotically efficient estimation under semi-parametric random censorship models," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 10-24.
    2. Austin, Matthew D. & Betensky, Rebecca A., 2014. "Eliminating bias due to censoring in Kendall’s tau estimators for quasi-independence of truncation and failure," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 16-26.
    3. Subramanian, Sundarraman & Bandyopadhyay, Dipankar, 2008. "Semiparametric left truncation and right censorship models with missing censoring indicators," Statistics & Probability Letters, Elsevier, vol. 78(16), pages 2572-2577, November.
    4. Sun, Liuquan, 2006. "The strong law under a semiparametric model for truncated and censored data," Statistics & Probability Letters, Elsevier, vol. 76(14), pages 1550-1558, August.
    5. Micha Mandel & Rebecca A. Betensky, 2007. "Testing Goodness of Fit of a Uniform Truncation Model," Biometrics, The International Biometric Society, vol. 63(2), pages 405-412, June.

  61. A S Mugglin & B P Carlin & L Zhu & E Conlon, 1999. "Bayesian Areal Interpolation, Estimation, and Smoothing: An Inferential Approach for Geographic Information Systems," Environment and Planning A, , vol. 31(8), pages 1337-1352, August.

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    1. Jonathan R. Bradley & Christopher K. Wikle & Scott H. Holan, 2016. "Bayesian Spatial Change of Support for Count-Valued Survey Data With Application to the American Community Survey," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 472-487, April.
    2. Groß Marcus & Kreutzmann Ann-Kristin & Rendtel Ulrich & Schmid Timo & Tzavidis Nikos, 2020. "Switching Between Different Non-Hierachical Administrative Areas via Simulated Geo-Coordinates: A Case Study for Student Residents in Berlin," Journal of Official Statistics, Sciendo, vol. 36(2), pages 297-314, June.
    3. Daisuke Murakami & Morito Tsutsumi, 2012. "Practical Spatial Statisics for Areal Interpolation," Environment and Planning B, , vol. 39(6), pages 1016-1033, December.
    4. Groß Marcus & Kreutzmann Ann-Kristin & Rendtel Ulrich & Schmid Timo & Tzavidis Nikos, 2020. "Switching Between Different Non-Hierachical Administrative Areas via Simulated Geo-Coordinates: A Case Study for Student Residents in Berlin," Journal of Official Statistics, Sciendo, vol. 36(2), pages 297-314, June.
    5. Christopher K. Wikle, 2003. "Hierarchical Models in Environmental Science," International Statistical Review, International Statistical Institute, vol. 71(2), pages 181-199, August.

  62. Fang, K. T. & Zhu, L. X. & Bentler, P. M., 1993. "A Necessary Test of Goodness of Fit for Sphericity," Journal of Multivariate Analysis, Elsevier, vol. 45(1), pages 34-55, April.

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    1. Albisetti, Isaia & Balabdaoui, Fadoua & Holzmann, Hajo, 2020. "Testing for spherical and elliptical symmetry," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    2. Zhu, Li-Xing & Neuhaus, Georg, 2003. "Conditional tests for elliptical symmetry," Journal of Multivariate Analysis, Elsevier, vol. 84(2), pages 284-298, February.
    3. Batsidis, Apostolos & Zografos, Konstantinos, 2013. "A necessary test of fit of specific elliptical distributions based on an estimator of Song’s measure," Journal of Multivariate Analysis, Elsevier, vol. 113(C), pages 91-105.
    4. Manzotti, A. & Pérez, Francisco J. & Quiroz, Adolfo J., 2002. "A Statistic for Testing the Null Hypothesis of Elliptical Symmetry," Journal of Multivariate Analysis, Elsevier, vol. 81(2), pages 274-285, May.
    5. Neuhaus, Georg & Zhu, Li-Xing, 1998. "Permutation Tests for Reflected Symmetry," Journal of Multivariate Analysis, Elsevier, vol. 67(2), pages 129-153, November.
    6. Yuan, Ke-Hai & Bentler, Peter M., 2000. "Inferences on Correlation Coefficients in Some Classes of Nonnormal Distributions," Journal of Multivariate Analysis, Elsevier, vol. 72(2), pages 230-248, February.
    7. Iwashita, Toshiya & Klar, Bernhard & Amagai, Moe & Hashiguchi, Hiroki, 2017. "A test procedure for uniformity on the Stiefel manifold based on projection," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 89-96.
    8. Huiwen Wang & Qiang Liu, 1998. "Forecast modelling for rotations of principal axes of multidimensional data sets," Computational Statistics & Data Analysis, Elsevier, vol. 27(3), pages 345-354, May.

  63. Zhang, J. & Zhu, L. X. & Cheng, P., 1993. "Exponential Bounds for the Uniform Deviation of a Kind of Empirical Processes, II," Journal of Multivariate Analysis, Elsevier, vol. 47(2), pages 250-268, November.

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    1. Hengjian, Cui, 1997. "The laws of the iterated logarithm for two kinds of PP statistics," Statistics & Probability Letters, Elsevier, vol. 32(3), pages 235-243, March.

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