Familywise Error Rate Control by Interactive Unmasking

Duan, Boyan; Ramdas, Aaditya; Wasserman, Larry

doi:10.48550/arxiv.2002.08545

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Karlin and Rinott (1980); Hochberg (1988); Benjamini and Yekutieli (2001); Romano et al (2010)). We refer the readers to Guo et al (2014); Duan et al (2020) and the references therein for a survey of these methods.…”

Section: Improving Assumption-free Guaranteesmentioning

confidence: 99%

Derandomizing Knockoffs

Ren¹,

Wei²,

Candès³

2020

Preprint

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Model-X knockoffs is a general procedure that can leverage any feature importance measure to produce a variable selection algorithm, which discovers true effects while rigorously controlling the number or fraction of false positives. Model-X knockoffs is a randomized procedure which relies on the one-time construction of synthetic (random) variables. This paper introduces a derandomization method by aggregating the selection results across multiple runs of the knockoffs algorithm. The derandomization step is designed to be flexible and can be adapted to any variable selection base procedure to yield stable decisions without compromising statistical power. When applied to the base procedure of Janson et al. ( 2016), we prove that derandomized knockoffs controls both the per family error rate (PFER) and the k family-wise error rate (k-FWER). Further, we carry out extensive numerical studies demonstrating tight type-I error control and markedly enhanced power when compared with alternative variable selection algorithms. Finally, we apply our approach to multi-stage genome-wide association studies of prostate cancer and report locations on the genome that are significantly associated with the disease. When cross-referenced with other studies, we find that the reported associations have been replicated.

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