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arXiv · 2310.10373

False Discovery Proportion control for aggregated Knockoffs

Alexandre Blain·Bertrand Thirion·Olivier Grisel·Pierre Neuvial
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Abstract

Controlled variable selection is an important analytical step in various scientific fields, such as brain imaging or genomics. In these high-dimensional data settings, considering too many variables leads to poor models and high costs, hence the need for statistical guarantees on false positives. Knockoffs are a popular statistical tool for conditional variable selection in high dimension. However, they control for the expected proportion of false discoveries (FDR) and not their actual proportion (FDP). We present a new method, KOPI, that controls the proportion of false discoveries for Knockoff-based inference. The proposed method also relies on a new type of aggregation to address the undesirable randomness associated with classical Knockoff inference. We demonstrate FDP control and substantial power gains over existing Knockoff-based methods in various simulation settings and achieve good sensitivity/specificity tradeoffs on brain imaging and genomic data.

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Alexandre Blain, Bertrand Thirion, Olivier Grisel, Pierre Neuvial. 2023-10-16. False Discovery Proportion control for aggregated Knockoffs. https://arxiv.org/abs/2310.10373

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