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Toshio Honda

Personal Details

First Name:Toshio
Middle Name:
Last Name:Honda
Suffix:
RePEc Short-ID:pho529
[This author has chosen not to make the email address public]
https://hri.ad.hit-u.ac.jp/html/449_profile_en.html

Affiliation

Graduate School of Economics/Faculty of Economics
Hitotsubashi University

Tokyo, Japan
http://www.econ.hit-u.ac.jp/
RePEc:edi:fehitjp (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. HONDA, Toshio & 本田, 敏雄 & ING, Ching-Kang & WU, Wei-Ying, 2017. "Adaptively weighted group Lasso for semiparametric quantile regression models," Discussion Papers 2017-04, Graduate School of Economics, Hitotsubashi University.
  2. Honda, Toshio & Härdle, Wolfgang Karl, 2012. "Variable selection in Cox regression models with varying coefficients," SFB 649 Discussion Papers 2012-061, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  3. Toshio Honda, 2011. "Nonparametric LAD Cointegrating Regression," Global COE Hi-Stat Discussion Paper Series gd11-207, Institute of Economic Research, Hitotsubashi University.
  4. Toshio Honda, 2010. "Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors," Global COE Hi-Stat Discussion Paper Series gd10-157, Institute of Economic Research, Hitotsubashi University.
  5. Toshio Honda, 2009. "Nonparametric regression for dependent data in the errors-in-variables problem," Global COE Hi-Stat Discussion Paper Series gd09-092, Institute of Economic Research, Hitotsubashi University.
  6. Honda, Toshio & 本田, 敏雄, 2007. "Nonparametric Estimation of Conditional Medians for Linear and Related Processes," Discussion Papers 2005-04, Graduate School of Economics, Hitotsubashi University.
  7. Honda, Toshio & 本田, 敏雄, 2007. "Estimation in Partial Linear Models under Long-Range Dependence," Discussion Papers 2007-07, Graduate School of Economics, Hitotsubashi University.
  8. Honda, Toshio & 本田, 敏雄, 2007. "Noncentral Limit Theorems for Bounded Functions of Linear Processes without Finite Mean," Discussion Papers 2006-22, Graduate School of Economics, Hitotsubashi University.
  9. Honda, Toshio & 本田, 敏雄, 2006. "Nonparametric Density Estimation for Linear Processes with Infinite Variance," Discussion Papers 2005-13, Graduate School of Economics, Hitotsubashi University.

Articles

  1. Honda, Toshio & Yabe, Ryota, 2017. "Variable selection and structure identification for varying coefficient Cox models," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 103-122.
  2. Ming-Yen Cheng & Toshio Honda & Jin-Ting Zhang, 2016. "Forward Variable Selection for Sparse Ultra-High Dimensional Varying Coefficient Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1209-1221, July.
  3. Toshio Honda, 2015. "Discussion," International Statistical Review, International Statistical Institute, vol. 83(1), pages 68-70, April.
  4. Toshio Honda, 2013. "Nonparametric quantile regression with heavy-tailed and strongly dependent errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(1), pages 23-47, February.
  5. Honda, Toshio, 2013. "Nonparametric LAD cointegrating regression," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 150-162.
  6. Toshio Honda, 2010. "Nonparametric estimation of conditional medians for linear and related processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 995-1021, December.
  7. Toshio Honda, 2009. "Nonparametric density estimation for linear processes with infinite variance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(2), pages 413-439, June.
  8. Toshio Honda, 2005. "Estimation in additive cox models by marginal integration," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 403-423, September.
  9. Toshio Honda, 2004. "Nonparametric regression with current status data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 56(1), pages 49-72, March.
  10. Toshio Honda, 2000. "Nonparametric Estimation of a Conditional Quantile for α-Mixing Processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 459-470, September.
  11. Toshio Honda, 2000. "Nonparametric Density Estimation for a Long-Range Dependent Linear Process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(4), pages 599-611, December.
  12. Honda, Toshio, 1991. "Minimax estimators in the manova model for arbitrary quadratic loss and unknown covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 36(1), pages 113-120, January.

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. HONDA, Toshio & 本田, 敏雄 & ING, Ching-Kang & WU, Wei-Ying, 2017. "Adaptively weighted group Lasso for semiparametric quantile regression models," Discussion Papers 2017-04, Graduate School of Economics, Hitotsubashi University.

    Cited by:

    1. Honda, Toshio & 本田, 敏雄 & Lin, Chien-Tong, 2022. "Forward variable selection for ultra-high dimensional quantile regression models," Discussion Papers 2021-02, Graduate School of Economics, Hitotsubashi University.

  2. Honda, Toshio & Härdle, Wolfgang Karl, 2012. "Variable selection in Cox regression models with varying coefficients," SFB 649 Discussion Papers 2012-061, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    Cited by:

    1. HONDA, Toshio & 本田, 敏雄 & YABE, Ryota & 矢部, 竜太, 2017. "Variable selection and structure identification for varying coefficient Cox models," Discussion Papers 2016-05, Graduate School of Economics, Hitotsubashi University.
    2. Ling Zhou & Lu Tang & Angela T. Song & Diane M. Cibrik & Peter X.-K. Song, 2017. "A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 431-452, December.
    3. Honda, Toshio & 本田, 敏雄, 2019. "The de-biased group Lasso estimation for varying coefficient models," Discussion Papers 2018-04, Graduate School of Economics, Hitotsubashi University.
    4. Toshio Honda, 2021. "The de-biased group Lasso estimation for varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(1), pages 3-29, February.

  3. Toshio Honda, 2011. "Nonparametric LAD Cointegrating Regression," Global COE Hi-Stat Discussion Paper Series gd11-207, Institute of Economic Research, Hitotsubashi University.

    Cited by:

    1. YABE, Ryota & 矢部, 竜太, 2014. "Empirical Likelihood Confidence Intervals for Nonparametric Nonlinear Nonstationary Regression Models," Discussion Papers 2014-20, Graduate School of Economics, Hitotsubashi University.
    2. Tu, Yundong & Liang, Han-Ying & Wang, Qiying, 2022. "Nonparametric inference for quantile cointegrations with stationary covariates," Journal of Econometrics, Elsevier, vol. 230(2), pages 453-482.

  4. Toshio Honda, 2010. "Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors," Global COE Hi-Stat Discussion Paper Series gd10-157, Institute of Economic Research, Hitotsubashi University.

    Cited by:

    1. Haowen Bao & Zongwu Cai & Yuying Sun & Shouyang Wang, 2023. "Penalized Model Averaging for High Dimensional Quantile Regressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202302, University of Kansas, Department of Economics, revised Jan 2023.
    2. Wang, Qiao, 2023. "A simple nonparametric conditional quantile estimator for time series with thin tails," Economics Letters, Elsevier, vol. 232(C).

  5. Toshio Honda, 2009. "Nonparametric regression for dependent data in the errors-in-variables problem," Global COE Hi-Stat Discussion Paper Series gd09-092, Institute of Economic Research, Hitotsubashi University.

    Cited by:

    1. Igor S. Borisov & Yuliana Yu. Linke & Pavel S. Ruzankin, 2021. "Universal weighted kernel-type estimators for some class of regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(2), pages 141-166, February.
    2. Mynbaev, Kairat & Martins-Filho, Carlos, 2015. "Consistency and asymptotic normality for a nonparametric prediction under measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 166-188.

  6. Honda, Toshio & 本田, 敏雄, 2007. "Nonparametric Estimation of Conditional Medians for Linear and Related Processes," Discussion Papers 2005-04, Graduate School of Economics, Hitotsubashi University.

    Cited by:

    1. Toshio Honda, 2010. "Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors," Global COE Hi-Stat Discussion Paper Series gd10-157, Institute of Economic Research, Hitotsubashi University.
    2. Honda, Toshio, 2013. "Nonparametric LAD cointegrating regression," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 150-162.

  7. Honda, Toshio & 本田, 敏雄, 2006. "Nonparametric Density Estimation for Linear Processes with Infinite Variance," Discussion Papers 2005-13, Graduate School of Economics, Hitotsubashi University.

    Cited by:

    1. Toshio Honda, 2010. "Nonparametric estimation of conditional medians for linear and related processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 995-1021, December.
    2. Toshio Honda, 2010. "Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors," Global COE Hi-Stat Discussion Paper Series gd10-157, Institute of Economic Research, Hitotsubashi University.
    3. Chang, Yoosoon & Kim, Chang Sik & Park, Joon Y., 2016. "Nonstationarity in time series of state densities," Journal of Econometrics, Elsevier, vol. 192(1), pages 152-167.

Articles

  1. Honda, Toshio & Yabe, Ryota, 2017. "Variable selection and structure identification for varying coefficient Cox models," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 103-122.

    Cited by:

    1. Ngai Hang Chan & Linhao Gao & Wilfredo Palma, 2022. "Simultaneous variable selection and structural identification for time‐varying coefficient models," Journal of Time Series Analysis, Wiley Blackwell, vol. 43(4), pages 511-531, July.
    2. Honda, Toshio & 本田, 敏雄, 2019. "The de-biased group Lasso estimation for varying coefficient models," Discussion Papers 2018-04, Graduate School of Economics, Hitotsubashi University.
    3. Toshio Honda, 2021. "The de-biased group Lasso estimation for varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(1), pages 3-29, February.
    4. Akira Shinkyu, 2023. "Forward Selection for Feature Screening and Structure Identification in Varying Coefficient Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 485-511, February.

  2. Ming-Yen Cheng & Toshio Honda & Jin-Ting Zhang, 2016. "Forward Variable Selection for Sparse Ultra-High Dimensional Varying Coefficient Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1209-1221, July.

    Cited by:

    1. 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.
    2. Tu, Yundong & Xie, Xinling, 2023. "Penetrating sporadic return predictability," Journal of Econometrics, Elsevier, vol. 237(1).
    3. Honda, Toshio & 本田, 敏雄, 2019. "The de-biased group Lasso estimation for varying coefficient models," Discussion Papers 2018-04, Graduate School of Economics, Hitotsubashi University.
    4. Toshio Honda, 2021. "The de-biased group Lasso estimation for varying coefficient models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(1), pages 3-29, February.
    5. Eun Ryung Lee & Seyoung Park & Sang Kyu Lee & Hyokyoung G. Hong, 2023. "Quantile forward regression for high-dimensional survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 769-806, October.
    6. Hong, Hyokyoung G. & Zheng, Qi & Li, Yi, 2019. "Forward regression for Cox models with high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 268-290.
    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. Honda, Toshio & 本田, 敏雄 & Lin, Chien-Tong, 2022. "Forward variable selection for ultra-high dimensional quantile regression models," Discussion Papers 2021-02, Graduate School of Economics, Hitotsubashi University.
    9. HONDA, Toshio & 本田, 敏雄 & ING, Ching-Kang & WU, Wei-Ying, 2017. "Adaptively weighted group Lasso for semiparametric quantile regression models," Discussion Papers 2017-04, Graduate School of Economics, Hitotsubashi University.
    10. Yang, Guangren & Zhang, Ling & Li, Runze & Huang, Yuan, 2019. "Feature screening in ultrahigh-dimensional varying-coefficient Cox model," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 284-297.
    11. Lu, Jun & Lin, Lu, 2018. "Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 242-254.
    12. Haofeng Wang & Hongxia Jin & Xuejun Jiang & Jingzhi Li, 2022. "Model Selection for High Dimensional Nonparametric Additive Models via Ridge Estimation," Mathematics, MDPI, vol. 10(23), pages 1-22, December.
    13. Toshio Honda & Chien-Tong Lin, 2023. "Forward variable selection for ultra-high dimensional quantile regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 393-424, June.
    14. Akira Shinkyu, 2023. "Forward Selection for Feature Screening and Structure Identification in Varying Coefficient Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 485-511, February.

  3. Toshio Honda, 2013. "Nonparametric quantile regression with heavy-tailed and strongly dependent errors," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(1), pages 23-47, February.
    See citations under working paper version above.
  4. Honda, Toshio, 2013. "Nonparametric LAD cointegrating regression," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 150-162.
    See citations under working paper version above.
  5. Toshio Honda, 2010. "Nonparametric estimation of conditional medians for linear and related processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(6), pages 995-1021, December.
    See citations under working paper version above.
  6. Toshio Honda, 2009. "Nonparametric density estimation for linear processes with infinite variance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(2), pages 413-439, June.
    See citations under working paper version above.
  7. Toshio Honda, 2005. "Estimation in additive cox models by marginal integration," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 403-423, September.

    Cited by:

    1. Lin Liu & Jianbo Li & Riquan Zhang, 2014. "General partially linear additive transformation model with right-censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(10), pages 2257-2269, October.

  8. Toshio Honda, 2000. "Nonparametric Estimation of a Conditional Quantile for α-Mixing Processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 459-470, September.

    Cited by:

    1. Cheng, Yebin & De Gooijer, Jan & Zerom, Dawit, 2009. "Efficient Estimation of an Additive Quantile Regression Model," MPRA Paper 14388, University Library of Munich, Germany.
    2. Cai, Zongwu & Xu, Xiaoping, 2008. "Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1595-1608.
    3. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2017. "Extreme M-quantiles as risk measures: From L1 to Lp optimization," TSE Working Papers 17-841, Toulouse School of Economics (TSE).
    4. Marc Hallin & Zudi Lu & Davy Paindaveine & Miroslav Siman, 2012. "Local Constant and Local Bilinear Multiple-Output Quantile Regression," Working Papers ECARES ECARES 2012-003, ULB -- Universite Libre de Bruxelles.
    5. Stephen C. Smith & Sungil Kwak, 2011. "Regional Agricultural Endowments and Shifts of Poverty Trap Equilibria: Evidence from Ethiopian Panel Data," Working Papers 2011-01, The George Washington University, Institute for International Economic Policy.
    6. Toshio Honda, 2010. "Nonparametric Quantile Regression with Heavy-Tailed and Strongly Dependent Errors," Global COE Hi-Stat Discussion Paper Series gd10-157, Institute of Economic Research, Hitotsubashi University.
    7. Dingshi Tian & Zongwu Cai & Ying Fang, 2018. "Econometric Modeling of Risk Measures: A Selective Review of the Recent Literature," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 201807, University of Kansas, Department of Economics, revised Oct 2018.
    8. Honda, Toshio, 2013. "Nonparametric LAD cointegrating regression," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 150-162.
    9. Zongwu Cai & Xiyuan Liu, 2020. "A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202017, University of Kansas, Department of Economics, revised Oct 2020.
    10. Yebin Cheng & Jan G. de Gooijer, 2005. "Bahadur Representation for the Nonparametric M-Estimator Under Alpha-mixing Dependence," Tinbergen Institute Discussion Papers 05-067/4, Tinbergen Institute.
    11. Kim, Seonjin & Zhao, Zhibiao & Shao, Xiaofeng, 2015. "Nonparametric functional central limit theorem for time series regression with application to self-normalized confidence interval," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 277-290.
    12. Leorato, Samantha & Peracchi, Franco & Tanase, Andrei V., 2012. "Asymptotically efficient estimation of the conditional expected shortfall," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 768-784.
    13. Pavel Boček & Miroslav Šiman, 2017. "On weighted and locally polynomial directional quantile regression," Computational Statistics, Springer, vol. 32(3), pages 929-946, September.
    14. Ioannides, D. A., 2004. "Fixed design regression quantiles for time series," Statistics & Probability Letters, Elsevier, vol. 68(3), pages 235-245, July.
    15. Lemdani, Mohamed & Ould-Saïd, Elias & Poulin, Nicolas, 2009. "Asymptotic properties of a conditional quantile estimator with randomly truncated data," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 546-559, March.
    16. Ould-SaI¨d, Elias, 2006. "A strong uniform convergence rate of kernel conditional quantile estimator under random censorship," Statistics & Probability Letters, Elsevier, vol. 76(6), pages 579-586, March.
    17. Racine, Jeffrey S. & Li, Kevin, 2017. "Nonparametric conditional quantile estimation: A locally weighted quantile kernel approach," Journal of Econometrics, Elsevier, vol. 201(1), pages 72-94.
    18. Han-Ying Liang & Jacobo Uña-Álvarez, 2012. "Empirical likelihood for conditional quantile with left-truncated and dependent data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(4), pages 765-790, August.
    19. Liang Chen, 2019. "Nonparametric Quantile Regressions for Panel Data Models with Large T," Papers 1911.01824, arXiv.org, revised Sep 2020.
    20. Yebin Cheng & Jan G. De Gooijer & Dawit Zerom, 2009. "Efficient Estimation of an Additive Quantile Regression," Tinbergen Institute Discussion Papers 09-104/4, Tinbergen Institute.
    21. Wang, Qiao, 2023. "A simple nonparametric conditional quantile estimator for time series with thin tails," Economics Letters, Elsevier, vol. 232(C).
    22. Zongwu Cai & Qi Li, 2013. "Some Recent Develop- ments on Nonparametric Econometrics," Working Papers 2013-10-14, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
    23. Lin, Zhengyan & Li, Degui, 2007. "Asymptotic normality for L1-norm kernel estimator of conditional median under association dependence," Journal of Multivariate Analysis, Elsevier, vol. 98(6), pages 1214-1230, July.
    24. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    25. Zongwu Cai & Zhijie Xiao, 2010. "Semiparametric Quantile Regression Estimation in Dynamic Models with Partially Varying Coefficients," Boston College Working Papers in Economics 761, Boston College Department of Economics.
    26. Komunjer, Ivana, 2013. "Quantile Prediction," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 961-994, Elsevier.

  9. Toshio Honda, 2000. "Nonparametric Density Estimation for a Long-Range Dependent Linear Process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(4), pages 599-611, December.

    Cited by:

    1. Sucharita Ghosh & Jan Beran, 2006. "On Estimating the Cumulant Generating Function of Linear Processes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(1), pages 53-71, March.
    2. Honda, Toshio & 本田, 敏雄, 2006. "Nonparametric Density Estimation for Linear Processes with Infinite Variance," Discussion Papers 2005-13, Graduate School of Economics, Hitotsubashi University.
    3. Hailin Sang & Yongli Sang, 2017. "Memory properties of transformations of linear processes," Statistical Inference for Stochastic Processes, Springer, vol. 20(1), pages 79-103, April.
    4. Timothy Fortune & Hailin Sang, 2020. "Shannon Entropy Estimation for Linear Processes," JRFM, MDPI, vol. 13(9), pages 1-13, September.

  10. Honda, Toshio, 1991. "Minimax estimators in the manova model for arbitrary quadratic loss and unknown covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 36(1), pages 113-120, January.

    Cited by:

    1. Kubokawa, T. & Srivastava, M. S., 2001. "Robust Improvement in Estimation of a Mean Matrix in an Elliptically Contoured Distribution," Journal of Multivariate Analysis, Elsevier, vol. 76(1), pages 138-152, January.
    2. T Matsuda & W E Strawderman, 2022. "Estimation under matrix quadratic loss and matrix superharmonicity [Shrinkage estimation with a matrix loss function]," Biometrika, Biometrika Trust, vol. 109(2), pages 503-519.
    3. Noda Kazuo & Wu Qi-Guang & Shimizu Kunio, 2002. "Γ-Μινιμαχιτυ Of A Generalized Bayes Unbiased Estimator In A Multivariate Linear Model," Statistics & Risk Modeling, De Gruyter, vol. 20(1-4), pages 53-66, April.
    4. Ahmed, S. E. & Krzanowski, W. J., 2004. "Biased estimation in a simple multivariate regression model," Computational Statistics & Data Analysis, Elsevier, vol. 45(4), pages 689-696, May.

More information

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Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 4 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (4) 2010-01-16 2011-02-26 2012-02-01 2017-04-16
  2. NEP-ETS: Econometric Time Series (1) 2012-02-01

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