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Forward variable selection for ultra-high dimensional quantile regression models

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  • Honda, Toshio
  • 本田, 敏雄
  • Lin, Chien-Tong

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Suggested Citation

  • 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.
    • Handle: RePEc:hit:econdp:2021-02
    Note: First version : August 2021, Second version：January 2022, This version：May 2022
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    File URL: https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/74078/070econDP21-02.pdf
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    References listed on IDEAS

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    1. Kong, Yinfei & Li, Yujie & Zerom, Dawit, 2019. "Screening and selection for quantile regression using an alternative measure of variable importance," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 435-455.
    2. Jiahua Chen & Zehua Chen, 2008. "Extended Bayesian information criteria for model selection with large model spaces," Biometrika, Biometrika Trust, vol. 95(3), pages 759-771.
    3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    4. Yuanshan Wu & Guosheng Yin, 2015. "Conditional quantile screening in ultrahigh-dimensional heterogeneous data," Biometrika, Biometrika Trust, vol. 102(1), pages 65-76.
    5. Eun Ryung Lee & Hohsuk Noh & Byeong U. Park, 2014. "Model Selection via Bayesian Information Criterion for Quantile Regression Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 216-229, March.
    6. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    7. Shan Luo & Zehua Chen, 2014. "Sequential Lasso Cum EBIC for Feature Selection With Ultra-High Dimensional Feature Space," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1229-1240, September.
    8. Wang, Hansheng, 2009. "Forward Regression for Ultra-High Dimensional Variable Screening," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1512-1524.
    9. 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.
    10. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    11. Lan Wang & Yichao Wu & Runze Li, 2012. "Quantile Regression for Analyzing Heterogeneity in Ultra-High Dimension," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 214-222, March.
    12. Qi Zheng & Hyokyoung G. Hong & Yi Li, 2020. "Building generalized linear models with ultrahigh dimensional features: A sequentially conditional approach," Biometrics, The International Biometric Society, vol. 76(1), pages 47-60, March.
    13. 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.
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    Cited by:

    1. 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.

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    Keywords

    forward procedure; check function; sparsity; screening consistency; stopping rule;
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