Open Access
April 2009 On the false discovery rate and an asymptotically optimal rejection curve
Helmut Finner, Thorsten Dickhaus, Markus Roters
Ann. Statist. 37(2): 596-618 (April 2009). DOI: 10.1214/07-AOS569

Abstract

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 motivation for this new curve and propose some procedures related to it. Then we introduce a set of possible assumptions and give a unifying short proof of FDR control for procedures based on Simes’ critical values, whereby certain types of dependency are allowed. This methodology of proof is then applied to other fixed rejection curves including the proposed new curve. Among others, we investigate the problem of finding least favorable parameter configurations such that the FDR becomes largest. We then derive a series of results concerning asymptotic FDR control for procedures based on the new curve and discuss several example procedures in more detail. A main result will be an asymptotic optimality statement for various procedures based on the new curve in the class of fixed rejection curves. Finally, we briefly discuss strict FDR control for a finite number of hypotheses.

Citation

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Helmut Finner. Thorsten Dickhaus. Markus Roters. "On the false discovery rate and an asymptotically optimal rejection curve." Ann. Statist. 37 (2) 596 - 618, April 2009. https://doi.org/10.1214/07-AOS569

Information

Published: April 2009
First available in Project Euclid: 10 March 2009

zbMATH: 1162.62068
MathSciNet: MR2502644
Digital Object Identifier: 10.1214/07-AOS569

Subjects:
Primary: 62F05 , 62J15
Secondary: 60F99 , 62F03

Keywords: Crossing point , extended Glivenko–Cantelli theorem , false discovery proportion , False discovery rate , familywise error rate , least favorable configurations , Multiple comparisons , multiple test procedure , order statistics , positive regression dependent , step-up test , step-up-down test

Rights: Copyright © 2009 Institute of Mathematical Statistics

Vol.37 • No. 2 • April 2009
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