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Empirical bayes methods and false discovery rates for microarrays
Author(s) -
Efron Bradley,
Tibshirani Robert
Publication year - 2002
Publication title -
genetic epidemiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.301
H-Index - 98
eISSN - 1098-2272
pISSN - 0741-0395
DOI - 10.1002/gepi.1124
Subject(s) - wilcoxon signed rank test , false discovery rate , frequentist inference , bayes' theorem , multiple comparisons problem , bayesian probability , statistics , inference , computer science , statistic , bayes factor , test statistic , statistical hypothesis testing , a priori and a posteriori , sample size determination , bayesian inference , econometrics , machine learning , mathematics , artificial intelligence , biology , biochemistry , philosophy , epistemology , gene , mann–whitney u test
In a classic two‐sample problem, one might use Wilcoxon's statistic to test for a difference between treatment and control subjects. The analogous microarray experiment yields thousands of Wilcoxon statistics, one for each gene on the array, and confronts the statistician with a difficult simultaneous inference situation. We will discuss two inferential approaches to this problem: an empirical Bayes method that requires very little a priori Bayesian modeling, and the frequentist method of “false discovery rates” proposed by Benjamini and Hochberg in 1995. It turns out that the two methods are closely related and can be used together to produce sensible simultaneous inferences. Genet. Epidemiol. 23:70–86, 2002. © 2002 Wiley‐Liss, Inc.