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A sequential feature selection procedure for high-dimensional Cox proportional hazards model

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  • Ke Yu

    (Shanghai Jiao Tong University)

  • Shan Luo

    (Shanghai Jiao Tong University)

Abstract

Feature selection for the high-dimensional Cox proportional hazards model (Cox model) is very important in many microarray genetic studies. In this paper, we propose a sequential feature selection procedure for this model. We define a novel partial profile score to assess the impact of unselected features conditional on the current model, significant features are thereby added into the model sequentially, and the Extended Bayesian Information Criteria (EBIC) is adopted as a stopping rule. Under mild conditions, we show that this procedure is selection consistent. Extensive simulation studies and two real data applications are conducted to demonstrate the advantage of our proposed procedure over several representative approaches.

Suggested Citation

  • Ke Yu & Shan Luo, 2022. "A sequential feature selection procedure for high-dimensional Cox proportional hazards model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(6), pages 1109-1142, December.
    • Handle: RePEc:spr:aistmt:v:74:y:2022:i:6:d:10.1007_s10463-022-00824-8
    DOI: 10.1007/s10463-022-00824-8
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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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