Inequalities for the false discovery rate (FDR) under dependence
Philipp Heesen, Arnold Janssen

TL;DR
This paper derives bounds for the false discovery rate (FDR) under various dependence structures of p-values, introduces new step up tests, and discusses conditions for FDR control, with applications to dependent genome data.
Contribution
It provides new inequalities and bounds for FDR under dependence, introduces adaptive step up tests, and explores the impact of dependence structures on FDR control.
Findings
Sharper FDR results under reverse martingale dependence than PRDS
Familywise error rate can differ from alpha in multivariate normal models
Modified Storey estimator achieves finite sample control under dependence
Abstract
Inequalities are key tools to prove FDR control of a multiple test. The present paper studies upper and lower bounds for the FDR under various dependence structures of p-values, namely independence, reverse martingale dependence and positive regression dependence on the subset (PRDS) of true null hypotheses. The inequalities are based on exact finite sample formulas which are also of interest for independent uniformly distributed p-values under the null. As applications the asymptotic worst case FDR of step up and step down tests coming from an non-decreasing rejection curve is established. In addition, new step up tests are established and necessary conditions for the FDR control are discussed. The reverse martingale models yield sharper FDR results than the PRDS models. Already in certain multivariate normal dependence models the familywise error rate of the Benjamini Hochberg step up…
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