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Model-free feature screening for ultra-high dimensional competing risks data

Author

Listed:
  • Chen, Xiaolin
  • Zhang, Yahui
  • Liu, Yi
  • Chen, Xiaojing

Abstract

In this article, we propose model-free marginal and conditional feature screening approaches for ultra-high dimensional competing risks data. The suggested procedures possess ranking consistency and sure screening properties. Their finite-sample performances are verified through numerical studies.

Suggested Citation

  • Chen, Xiaolin & Zhang, Yahui & Liu, Yi & Chen, Xiaojing, 2020. "Model-free feature screening for ultra-high dimensional competing risks data," Statistics & Probability Letters, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:stapro:v:164:y:2020:i:c:s0167715220301188
    DOI: 10.1016/j.spl.2020.108815
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    References listed on IDEAS

    as
    1. Chen, Xiaolin & Zhang, Yahui & Chen, Xiaojing & Liu, Yi, 2019. "A simple model-free survival conditional feature screening," Statistics & Probability Letters, Elsevier, vol. 146(C), pages 156-160.
    2. Chen, Xiaolin & Chen, Xiaojing & Wang, Hong, 2018. "Robust feature screening for ultra-high dimensional right censored data via distance correlation," Computational Statistics & Data Analysis, Elsevier, vol. 119(C), pages 118-138.
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    Citations

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    Cited by:

    1. Tian, Bing & Liu, Zili & Wang, Hong, 2022. "Non-marginal feature screening for varying coefficient competing risks model," Statistics & Probability Letters, Elsevier, vol. 190(C).

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