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Tuning parameter selection in high dimensional penalized likelihood

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Cited by:

  1. Zhong, Yan & Sang, Huiyan & Cook, Scott J. & Kellstedt, Paul M., 2023. "Sparse spatially clustered coefficient model via adaptive regularization," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
  2. Caner, Mehmet & Kock, Anders Bredahl, 2018. "Asymptotically honest confidence regions for high dimensional parameters by the desparsified conservative Lasso," Journal of Econometrics, Elsevier, vol. 203(1), pages 143-168.
  3. Zhenghao Zeng & Yuqi Gu & Gongjun Xu, 2023. "A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 580-612, June.
  4. Chen, J. & Li, D. & Li, Y. & Linton, O. B., 2022. "Estimating Time-Varying Networks for High-Dimensional Time Series," Janeway Institute Working Papers 2231, Faculty of Economics, University of Cambridge.
  5. Awijen, Haithem & Ben Zaied, Younes & Ben Lahouel, Béchir & Khlifi, Foued, 2023. "Machine learning for US cross-industry return predictability under information uncertainty," Research in International Business and Finance, Elsevier, vol. 64(C).
  6. Tae-Hwy Lee & Ekaterina Seregina, 2020. "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers 202024, University of California at Riverside, Department of Economics.
  7. Fan, Rui & Lee, Ji Hyung & Shin, Youngki, 2023. "Predictive quantile regression with mixed roots and increasing dimensions: The ALQR approach," Journal of Econometrics, Elsevier, vol. 237(2).
  8. Alexander Chudik & George Kapetanios & M. Hashem Pesaran, 2016. "Big data analytics: a new perspective," Globalization Institute Working Papers 268, Federal Reserve Bank of Dallas.
  9. Siem Jan Koopman & Julia Schaumburg & Quint Wiersma, 2021. "Joint Modelling and Estimation of Global and Local Cross-Sectional Dependence in Large Panels," Tinbergen Institute Discussion Papers 21-008/III, Tinbergen Institute.
  10. Kock, Anders Bredahl, 2016. "Oracle inequalities, variable selection and uniform inference in high-dimensional correlated random effects panel data models," Journal of Econometrics, Elsevier, vol. 195(1), pages 71-85.
  11. Peisong Han & Linglong Kong & Jiwei Zhao & Xingcai Zhou, 2019. "A general framework for quantile estimation with incomplete data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 305-333, April.
  12. Burman, Prabir & Paul, Debashis, 2017. "Smooth predictive model fitting in regression," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 165-179.
  13. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
    • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
  14. Guo, Xiao & Chen, Yu & Tang, Cheng Yong, 2023. "Information criteria for latent factor models: A study on factor pervasiveness and adaptivity," Journal of Econometrics, Elsevier, vol. 233(1), pages 237-250.
  15. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018. "A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models," Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.
  16. Hui Xiao & Yiguo Sun, 2019. "On Tuning Parameter Selection in Model Selection and Model Averaging: A Monte Carlo Study," JRFM, MDPI, vol. 12(3), pages 1-16, June.
  17. Jianqing Fan & Han Liu & Yang Ning & Hui Zou, 2017. "High dimensional semiparametric latent graphical model for mixed data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 405-421, March.
  18. Shi, Chengchun & Lu, Wenbin & Song, Rui, 2019. "Determining the number of latent factors in statistical multi-relational learning," LSE Research Online Documents on Economics 102110, London School of Economics and Political Science, LSE Library.
  19. 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.
  20. Ma, Binfeng & Wang, Xiaofang, 2023. "How does green floating bond and financial sector readiness promote green economic growth evidence from China," Resources Policy, Elsevier, vol. 85(PB).
  21. Kaixu Yang & Tapabrata Maiti, 2022. "Ultrahigh‐dimensional generalized additive model: Unified theory and methods," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 917-942, September.
  22. Yuta Umezu & Yusuke Shimizu & Hiroki Masuda & Yoshiyuki Ninomiya, 2019. "AIC for the non-concave penalized likelihood method," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(2), pages 247-274, April.
  23. Zhao, Jing, 2022. "Exploring the influence of the main factors on the crude oil price volatility: An analysis based on GARCH-MIDAS model with Lasso approach," Resources Policy, Elsevier, vol. 79(C).
  24. Mehmet Caner & Anders Bredahl Kock, 2016. "Oracle Inequalities for Convex Loss Functions with Nonlinear Targets," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1377-1411, December.
  25. Quynh Van Nong & Chi Tim Ng, 2021. "Clustering of subsample means based on pairwise L1 regularized empirical likelihood," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(1), pages 135-174, February.
  26. Li, Jun & Wang, Huijun & Yu, Jianfeng, 2021. "Aggregate expected investment growth and stock market returns," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 618-638.
  27. Zhang, Tonglin, 2024. "Variables selection using L0 penalty," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
  28. O-Chia Chuang & Rangan Gupta & Christian Pierdzioch & Buliao Shu, 2024. "Financial Uncertainty and Gold Market Volatility: Evidence from a GARCH-MIDAS Approach with Variable Selection," Working Papers 202441, University of Pretoria, Department of Economics.
  29. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
  30. Yang Feng & Qingfeng Liu, 2020. "Nested Model Averaging on Solution Path for High-dimensional Linear Regression," Papers 2005.08057, arXiv.org.
  31. O-Chia Chuang & Chenxu Yang, 2022. "Identifying the Determinants of Crude Oil Market Volatility by the Multivariate GARCH-MIDAS Model," Energies, MDPI, vol. 15(8), pages 1-14, April.
  32. Müller, Dominik & Czado, Claudia, 2019. "Dependence modelling in ultra high dimensions with vine copulas and the Graphical Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 211-232.
  33. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
  34. Siddhartha Nandy & Chae Young Lim & Tapabrata Maiti, 2017. "Additive model building for spatial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 779-800, June.
  35. David Mazáček & Jiří Panoš, 2022. "Key determinants of new residential real estate prices in Prague," FFA Working Papers 5.002, Prague University of Economics and Business, revised 11 Apr 2023.
  36. Dong, Ruipeng & Li, Daoji & Zheng, Zemin, 2021. "Parallel integrative learning for large-scale multi-response regression with incomplete outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
  37. Jessie Jeng, X., 2016. "Detecting weak signals in high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 147(C), pages 234-246.
  38. Huang, Shih-Ting & Xie, Fang & Lederer, Johannes, 2021. "Tuning-free ridge estimators for high-dimensional generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
  39. Mingli Chen & Kengo Kato & Chenlei Leng, 2021. "Analysis of networks via the sparse β‐model," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(5), pages 887-910, November.
  40. Kwon, Sunghoon & Oh, Seungyoung & Lee, Youngjo, 2016. "The use of random-effect models for high-dimensional variable selection problems," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 401-412.
  41. Esra Ulasan & A. Özlem Önder, 2023. "Large portfolio optimisation approaches," Journal of Asset Management, Palgrave Macmillan, vol. 24(6), pages 485-497, October.
  42. Antoine Bichat & Christophe Ambroise & Mahendra Mariadassou, 2022. "Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process," Computational Statistics, Springer, vol. 37(3), pages 995-1013, July.
  43. 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.
  44. Tata Subba Rao & Granville Tunnicliffe Wilson & Ngai Hang Chan & Ye Lu & Chun Yip Yau, 2017. "Factor Modelling for High-Dimensional Time Series: Inference and Model Selection," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 285-307, March.
  45. Chun Wang, 2021. "Using Penalized EM Algorithm to Infer Learning Trajectories in Latent Transition CDM," Psychometrika, Springer;The Psychometric Society, vol. 86(1), pages 167-189, March.
  46. Lv, Shaogao & You, Mengying & Lin, Huazhen & Lian, Heng & Huang, Jian, 2018. "On the sign consistency of the Lasso for the high-dimensional Cox model," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 79-96.
  47. Paweł Teisseyre & Robert A. Kłopotek & Jan Mielniczuk, 2016. "Random Subspace Method for high-dimensional regression with the R package regRSM," Computational Statistics, Springer, vol. 31(3), pages 943-972, September.
  48. Piotr Pokarowski & Wojciech Rejchel & Agnieszka Sołtys & Michał Frej & Jan Mielniczuk, 2022. "Improving Lasso for model selection and prediction," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 831-863, June.
  49. 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.
  50. Katayama, Shota & Imori, Shinpei, 2014. "Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 132(C), pages 138-150.
  51. Wang, Yuejing & Ye, Wuyi & Jiang, Ying & Liu, Xiaoquan, 2024. "Volatility prediction for the energy sector with economic determinants: Evidence from a hybrid model," International Review of Financial Analysis, Elsevier, vol. 92(C).
  52. Jeon, Jong-June & Kwon, Sunghoon & Choi, Hosik, 2017. "Homogeneity detection for the high-dimensional generalized linear model," Computational Statistics & Data Analysis, Elsevier, vol. 114(C), pages 61-74.
  53. Zakariya Algamal & Muhammad Lee, 2015. "Adjusted Adaptive LASSO in High-dimensional Poisson Regression Model," Modern Applied Science, Canadian Center of Science and Education, vol. 9(4), pages 170-170, April.
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