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Biometrika Advance Access originally published online on September 26, 2008
Biometrika 2008 95(4):961-977; doi:10.1093/biomet/asn036
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© 2008 Biometrika Trust

Articles

Estimating the false discovery rate using the stochastic approximation algorithm

Faming Liang

Department of Statistics, Texas A&M University, College Station, Texas 77843, U.S.A., fliang{at}stat.tamu.edu

Jian Zhang

Institute of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, Kent CT2 7NF, U.K., j.zhang{at}kent.ac.uk

Received for publication 1 May 2006. Revision received 1 February 2008.
  Abstract

Testing of multiple hypotheses involves statistics that are strongly dependent in some applications, but most work on this subject is based on the assumption of independence. We propose a new method for estimating the false discovery rate of multiple hypothesis tests, in which the density of test scores is estimated parametrically by minimizing the Kullback–Leibler distance between the unknown density and its estimator using the stochastic approximation algorithm, and the false discovery rate is estimated using the ensemble averaging method. Our method is applicable under general dependence between test statistics. Numerical comparisons between our method and several competitors, conducted on simulated and real data examples, show that our method achieves more accurate control of the false discovery rate in almost all scenarios.

Key Words: Ensemble averaging • False discovery rate • Microarray data analysis • Multiple hypothesis testing • Stochastic approximation


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