Adaptive procedures for false discovery rate estimation
by Dr Kun Liang
Abstract: Multiple testing has generated a surging interest in recent years due to the wide availability of large and complex modern data sets. Much research focused on the false discovery rate (FDR) estimation and control, and adaptive procedures have particularly attracted growing attention. By incorporating good estimates of the proportion of true null hypotheses among all hypotheses, adaptive procedures have been shown to increase the power of detecting non-null hypotheses while maintaining the FDR. Most existing adaptive procedures rely on tuning parameters, which can be either assigned a priori (fixed) or estimated from data (dynamically). In this talk, I will first provide a finite sample proof of conservative point estimation for fixed adaptive FDR procedures. Then, I will present a general condition under which dynamic adaptive procedures can lead to conservative null proportion and FDR estimators. Simulation results show that a novel dynamic adaptive procedure achieves more power through smaller estimation errors for null proportion under independence and mild dependence conditions.
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