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In many fields of science, we observe a response variable together with a large number of potential explanatory variables, and would like to be able to discover which variables are truly associated wi...
This paper proposes a simple procedure to obtain monotone estimates of both the local and the tail false discovery rates that arise in large-scale multiple testing. The proposed monotonization natural...
We prove that the linear step-up procedure $\vp^{LSU}$ considered by Benjamini and Hochberg (1995) controls the false discovery rate (FDR) in the case of dependent $p$-values whose dependency structur...
When testing a large number of independent hypotheses, three different questions are of interest: are some hypotheses true alternatives? How many of them? Which of them? These questions give rise to...
We derive exact formulas for the false discovery rate (FDR) and the Power of any step-up and step-down procedures and for the s- th moment of the false discovery proportion (FDP) of any step-up pro...
In this paper we introduce and investigate a new rejection curve for asymptotic control of the false discovery rate (FDR) in multiple hypotheses testing problems. We first give a heuristic motivatio...
The false discovery rate (FDR) and false nondiscovery rate (FNDR) have received considerable attention in the literature on multiple testing. These performance measures are also appropriate for clas...
In large scale multiple testing, the use of an empirical null distribution rather than the theoretical null distribution can be critical for correct inference. This paper proposes a “mode matching” ...
Multivariate statistics are often available as well as necessary in hypothesis tests. We study how to use such statistics to control not only false discovery rate (FDR) but also positive FDR (pFDR) ...
The positive false discovery rate (pFDR) is a useful overall measure of errors for multiple hypothesis testing, especially when the underlying goal is to attain one or more discoveries. Control of p...

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