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VOL. 54 | 2007 Empirical Bayes methods for controlling the false discovery rate with dependent data


False discovery rate (FDR) has been widely used as an error measure in large scale multiple testing problems, but most research in the area has been focused on procedures for controlling the FDR based on independent test statistics or the properties of such procedures for test statistics with certain types of stochastic dependence. Based on an approach proposed in Tang and Zhang (2005), we further develop in this paper empirical Bayes methods for controlling the FDR with dependent data. We implement our methodology in a time series model and report the results of a simulation study to demonstrate the advantages of the empirical Bayes approach.


Published: 1 January 2007
First available in Project Euclid: 4 December 2007

MathSciNet: MR2459185

Digital Object Identifier: 10.1214/074921707000000111

Primary: 62C10 , 62C12 , 62C25 , 62H15

Keywords: Bayes rule , conditional false discovery rate , dependent data , Empirical Bayes , False discovery rate , most powerful test , Multiple comparisons , time series

Rights: Copyright © 2007, Institute of Mathematical Statistics


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