On the empirical bayes approach to the problem of multiple testing

Journal Article

We discuss the Empirical Bayes approach to the problem of multiple testing and compare it with a very popular frequentist method of Benjamini and Hochberg aimed at controlling the false discovery rate. Our main focus is the 'sparse mixture' case, when only a small proportion of tested hypotheses is expected to be false. The specific parametric model we consider is motivated by the application to detecting genes responsible for quantitative traits, but it can be used in a variety of other applications. We define some Parametric Empirical Bayes procedures for multiple testing and compare them with the Benjamini and Hochberg method using computer simulations. We explain some similarities between these two approaches by placing them within the same framework of threshold tests with estimated critical values. Copyright ©2007 John Wiley & Sons, Ltd.

Full Text

Duke Authors

Cited Authors

  • Bogdan, M; Ghosh, JK; Ochman, A; Tokdar, ST

Published Date

  • October 1, 2007

Published In

Volume / Issue

  • 23 / 6

Start / End Page

  • 727 - 739

Electronic International Standard Serial Number (EISSN)

  • 1099-1638

International Standard Serial Number (ISSN)

  • 0748-8017

Digital Object Identifier (DOI)

  • 10.1002/qre.876

Citation Source

  • Scopus