2020/11/24 by Luo, Dong, Ebadi, Arya, He, Yilun +3
#Applications (stat.AP) #FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.2011.11939
Recently, Barber and Candès 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 α, the FDP in the list of discoveries can significantly exceed α. 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 α 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.