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

TL;DR

This paper develops empirical Bayes methods to control the false discovery rate in large-scale multiple testing scenarios with dependent data, extending existing approaches to account for dependence structures.

Contribution

It introduces an empirical Bayes framework tailored for dependent data, implemented within a time series model, and demonstrates its effectiveness through simulations.

Findings

Empirical Bayes methods effectively control FDR with dependent data.

Simulation results show advantages over traditional methods.

Approach applicable to time series and potentially other dependent data structures.

Abstract

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.