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Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression

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  • Chaohui Guo

    (Chongqing University)

  • Hu Yang

    (Chongqing University)

  • Jing Lv

    (Chongqing University)

Abstract

In this paper, a new variable selection procedure based on weighted composite quantile regression is proposed for varying coefficient models with a diverging number of parameters. The proposed method is based on basis function approximation and the group SCAD penalty. The new estimation method can achieve both robustness and efficiency. Furthermore, the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation are established under some suitable assumptions. Finally, the finite sample behavior of the estimator is evaluated by simulation studies. In addition, some interesting extensions are made to separate constant coefficients from varying coefficients.

Suggested Citation

  • Chaohui Guo & Hu Yang & Jing Lv, 2017. "Robust variable selection in high-dimensional varying coefficient models based on weighted composite quantile regression," Statistical Papers, Springer, vol. 58(4), pages 1009-1033, December.
    • Handle: RePEc:spr:stpapr:v:58:y:2017:i:4:d:10.1007_s00362-015-0736-5
    DOI: 10.1007/s00362-015-0736-5
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    References listed on IDEAS

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    Cited by:

    1. Lin, Fangzheng & Tang, Yanlin & Zhu, Zhongyi, 2020. "Weighted quantile regression in varying-coefficient model with longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    2. Jun Zhao & Guan’ao Yan & Yi Zhang, 2022. "Robust estimation and shrinkage in ultrahigh dimensional expectile regression with heavy tails and variance heterogeneity," Statistical Papers, Springer, vol. 63(1), pages 1-28, February.

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