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Variable selection in censored quantile regression with high dimensional data

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Abstract

We propose a two-step variable selection procedure for censored quantile regression with high dimensional predictors. To account for censoring data in high dimensional case, we employ effective dimension reduction and the ideas of informative subset idea. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. Simulation study and real data analysis are conducted to evaluate the finite sample performance of the proposed approach.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 11401383, 11301391 and 11271080). The authors thank two anonymous reviewers for helpful comments.

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Authors and Affiliations

  1. College of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yali Fan

  2. School of Mathematical Sciences, Tongji University, Shanghai, 200092, China

    Yanlin Tang

  3. Department of Statistics, Fudan University, Shanghai, 200433, China

    Zhongyi Zhu

Authors
  1. Yali Fan
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  2. Yanlin Tang
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  3. Zhongyi Zhu
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Correspondence to Zhongyi Zhu.

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Fan, Y., Tang, Y. & Zhu, Z. Variable selection in censored quantile regression with high dimensional data. Sci. China Math. 61, 641–658 (2018). https://doi.org/10.1007/s11425-016-9016-7

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  • DOI: https://doi.org/10.1007/s11425-016-9016-7

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