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Forward variable selection for ultra-high dimensional quantile regression models

Author

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  • Toshio Honda

    (Hitotsubashi University)

  • Chien-Tong Lin

    (Feng Chia University)

Abstract

We propose forward variable selection procedures with a stopping rule for feature screening in ultra-high-dimensional quantile regression models. For such very large models, penalized methods do not work and some preliminary feature screening is necessary. We demonstrate the desirable theoretical properties of our forward procedures by taking care of uniformity w.r.t. subsets of covariates properly. The necessity of such uniformity is often overlooked in the literature. Our stopping rule suitably incorporates the model size at each stage. We also present the results of simulation studies and a real data application to show their good finite sample performances.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:aistmt:v:75:y:2023:i:3:d:10.1007_s10463-022-00849-z
    DOI: 10.1007/s10463-022-00849-z
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    References listed on IDEAS

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    Cited by:

    1. Yu, Ke & Luo, Shan, 2024. "Rank-based sequential feature selection for high-dimensional accelerated failure time models with main and interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).

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