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March 2010 An empirical Bayes mixture method for effect size and false discovery rate estimation
Omkar Muralidharan
Ann. Appl. Stat. 4(1): 422-438 (March 2010). DOI: 10.1214/09-AOAS276

Abstract

Many statistical problems involve data from thousands of parallel cases. Each case has some associated effect size, and most cases will have no effect. It is often important to estimate the effect size and the local or tail-area false discovery rate for each case. Most current methods do this separately, and most are designed for normal data. This paper uses an empirical Bayes mixture model approach to estimate both quantities together for exponential family data. The proposed method yields simple, interpretable models that can still be used nonparametrically. It can also estimate an empirical null and incorporate it fully into the model. The method outperforms existing effect size and false discovery rate estimation procedures in normal data simulations; it nearly acheives the Bayes error for effect size estimation. The method is implemented in an R package (mixfdr), freely available from CRAN.

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Omkar Muralidharan. "An empirical Bayes mixture method for effect size and false discovery rate estimation." Ann. Appl. Stat. 4 (1) 422 - 438, March 2010. https://doi.org/10.1214/09-AOAS276

Information

Published: March 2010
First available in Project Euclid: 11 May 2010

zbMATH: 1189.62004
MathSciNet: MR2758178
Digital Object Identifier: 10.1214/09-AOAS276

Keywords: effect size estimation , Empirical Bayes , empirical null , False discovery rate , mixture prior

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.4 • No. 1 • March 2010
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