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On the Empirical Bayes approach to the problem of multiple testing
Author(s) -
Bogdan Małgorzata,
Ghosh Jayanta K.,
Ochman Aleksandra,
Tokdar Surya T.
Publication year - 2007
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.876
Subject(s) - false discovery rate , bayes' theorem , frequentist inference , multiple comparisons problem , parametric statistics , bayes factor , computer science , bayesian probability , statistical hypothesis testing , nonparametric statistics , empirical research , machine learning , econometrics , artificial intelligence , statistics , mathematics , bayesian inference , biology , biochemistry , gene
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 ParametricEmpirical 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.