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False discovery control for penalized variable selections with high-dimensional covariates

Author

Listed:
  • He Kevin

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA)

  • Zhou Xiang

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA)

  • Jiang Hui

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA)

  • Wen Xiaoquan

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA)

  • Li Yi

    (Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA)

Abstract

Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors much exceeding the sample size. Penalized variable selection has emerged as a powerful and efficient dimension reduction tool. However, control of false discoveries (i.e. inclusion of irrelevant variables) for penalized high-dimensional variable selection presents serious challenges. To effectively control the fraction of false discoveries for penalized variable selections, we propose a false discovery controlling procedure. The proposed method is general and flexible, and can work with a broad class of variable selection algorithms, not only for linear regressions, but also for generalized linear models and survival analysis.

Suggested Citation

  • He Kevin & Zhou Xiang & Jiang Hui & Wen Xiaoquan & Li Yi, 2018. "False discovery control for penalized variable selections with high-dimensional covariates," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(6), pages 1-11, December.
    • Handle: RePEc:bpj:sagmbi:v:17:y:2018:i:6:p:11:n:3
    DOI: 10.1515/sagmb-2018-0038
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    References listed on IDEAS

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    3. Uno, Hajime & Cai, Tianxi & Tian, Lu & Wei, L.J., 2007. "Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 527-537, June.
    4. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
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