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Surrogate-variable-based model-free feature screening for survival data under the general censoring mechanism

Author

Listed:
  • Jing Zhang

    (Shanghai Lixin University of Accounting and Finance)

  • Qihua Wang

    (Chinese Academy of Sciences
    Zhejiang Gongshang University)

  • Xuan Wang

    (Zhejiang University)

Abstract

Feature screening has been seen as the first step in analyzing the ultrahigh-dimensional data with the censored survival time. In this article, we develop a surrogate-variable-based model-free feature screening approach for the censored data under the general censoring mechanism, where the censoring variable may depend on the survival variable and the covariates. This approach is developed by finding some observable variables whose active covariates contain the active covariates of the survival variable as a subset, respectively. Then, any existing model-free feature screening method with the sure screening property for full data can be applied to estimating the sets of the active covariates of the observable variables and hence the set of the active covariates of the survival variable. The sure screening property of the proposed approach is established, and its finite sample performances are demonstrated through some simulations. Further, we illustrate the proposed approach by analyzing two real datasets.

Suggested Citation

  • Jing Zhang & Qihua Wang & Xuan Wang, 2022. "Surrogate-variable-based model-free feature screening for survival data under the general censoring mechanism," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(2), pages 379-397, April.
    • Handle: RePEc:spr:aistmt:v:74:y:2022:i:2:d:10.1007_s10463-021-00801-7
    DOI: 10.1007/s10463-021-00801-7
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    References listed on IDEAS

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