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

He Kevin (), Zhou Xiang, Jiang Hui, Wen Xiaoquan and Li Yi
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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

Statistical Applications in Genetics and Molecular Biology, 2018, vol. 17, issue 6, 11

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.

Keywords: dimension reduction; false discovery; penalized regression; variable selection (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1515/sagmb-2018-0038

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