| Title: | Derandomizing Knockoffs |
| Journal: | Journal of the American Statistical Association |
| Published: | 14 Sep 2021 |
| DOI: | https://doi.org/10.1080/01621459.2021.1962720 |
| URL: | https://arxiv.org/pdf/2012.02717 |
| Citations: | 21 (12 in last 2 years) as of 8 Aug 2024 |
Publication 10268
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
Abstract
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 article 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 and Su, we prove that derandomized knockoffs controls both the per family error rate (PFER) and the k family-wise error rate (k-FWER). Furthermore, 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 multistage 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. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.</p>
3 Authors
- Zhimei Ren
- Yuting Wei
- Emmanuel Candès
1 Application
| Application ID | Title |
|---|---|
| 27837 | Statistical Methods for Large Scale Genetic Studies |
Enabling scientific discoveries that improve human health