Institute of Mathematical Statistics Lecture Notes - Monograph Series

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

Weihua Tang and Cun-Hui Zhang

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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.

Chapter information

Regina Liu, William Strawderman and Cun-Hui Zhang, eds., Complex Datasets and Inverse Problems: Tomography, Networks and Beyond (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2007), 151-160

First available in Project Euclid: 4 December 2007

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Mathematical Reviews number (MathSciNet)

Primary: 62H15: Hypothesis testing 62C10: Bayesian problems; characterization of Bayes procedures 62C12: Empirical decision procedures; empirical Bayes procedures 62C25: Compound decision problems

multiple comparisons false discovery rate conditional false discovery rate most powerful test Bayes rule empirical Bayes dependent data time series

Copyright © 2007, Institute of Mathematical Statistics


Tang, Weihua; Zhang, Cun-Hui. Empirical Bayes methods for controlling the false discovery rate with dependent data. Complex Datasets and Inverse Problems, 151--160, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2007. doi:10.1214/074921707000000111.

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