On the empirical bayes approach to the problem of multiple testing
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.
Duke Scholars
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Operations Research
- 0906 Electrical and Electronic Engineering
- 0102 Applied Mathematics
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Operations Research
- 0906 Electrical and Electronic Engineering
- 0102 Applied Mathematics