Empirical Bayes methods for controlling the false discovery rate with dependent data

Details Empirical Bayes methods for controlling the false discovery rate with dependent data Empirical Bayes methods for controlling the false discovery rate with dependent data

2007-08-07

arxiv.org/abs/0708.0978v1

IMS Lecture Notes Monograph Series 2007, Vol. 54, 151-160

Statistics

Methodology

Published at http://dx.doi.org/10.1214/074921707000000111 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/p

Scientific paper

10.1214/074921707000000111

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.

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