Conditional calibration for false discovery rate control under dependence

2020/07/20 by Fithian, William, Lei, Lihua · 4 citations
#FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Statistics Theory (math.ST)

paper · doi:10.48550/arxiv.2007.10438

Abstract

We introduce a new class of methods for finite-sample false discovery rate (FDR) control in multiple testing problems with dependent test statistics where the dependence is fully or partially known. Our approach separately calibrates a data-dependent p-value rejection threshold for each hypothesis, relaxing or tightening the threshold as appropriate to target exact FDR control. In addition to our general framework we propose a concrete algorithm, the dependence-adjusted Benjamini-Hochberg (dBH) procedure, which adaptively thresholds the q-value for each hypothesis. Under positive regression dependence the dBH procedure uniformly dominates the standard BH procedure, and in general it uniformly dominates the Benjamini-Yekutieli (BY) procedure (also known as BH with log correction). Simulations and real data examples illustrate power gains over competing approaches to FDR control under dependence.

Cited by

• Asymptotic Uniform False Discovery Rate Control for Inference of Time-varying Correlations
• Active Hypothesis Testing under Computational Budgets with Applications to GWAS and LLM
• Membership Inference Attacks with False Discovery Rate Control
• Controlling the false discovery rate under a non-parametric graphical dependence model

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