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

Author

Listed:
  • He Kevin
  • Zhou Xiang
  • Jiang Hui
  • Wen Xiaoquan
  • 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

    as
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