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Competition-based control of the false discovery proportion
- Publication Year :
- 2020
-
Abstract
- Recently, Barber and Cand\`es laid the theoretical foundation for a general framework for false discovery rate (FDR) control based on the notion of "knockoffs." A closely related FDR control methodology has long been employed in the analysis of mass spectrometry data, referred to there as "target-decoy competition" (TDC). However, any approach that aims to control the FDR, which is defined as the expected value of the false discovery proportion (FDP), suffers from a problem. Specifically, even when successfully controlling the FDR at level $\alpha$, the FDP in the list of discoveries can significantly exceed $\alpha$. We offer FDP-SD, a new procedure that rigorously controls the FDP in the competition (knockoff / TDC) setup by guaranteeing that the FDP is bounded by $\alpha$ at any desired confidence level. Compared with the just-published general framework of Katsevich and Ramdas, FDP-SD generally delivers more power and often substantially so in simulated as well as real data.<br />Comment: This revision focuses only on FDP-SD described in the original submission. A later submission will further develop the procedures for simultaneous bounds on the FDP
- Subjects :
- Statistics - Methodology
Statistics - Applications
Subjects
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2011.11939
- Document Type :
- Working Paper