Abstract
Abstract 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.
Keywords
Wilcoxon signed-rank testFalse discovery rateFrequentist inferenceBayes' theoremMultiple comparisons problemBayesian probabilityStatisticsInferenceComputer scienceStatisticBayes factorTest statisticStatistical hypothesis testingA priori and a posterioriSample size determinationBayesian inferenceEconometricsMachine learningMathematicsArtificial intelligenceBiology
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Publication Info
- Year
- 2002
- Type
- article
- Volume
- 23
- Issue
- 1
- Pages
- 70-86
- Citations
- 682
- Access
- Closed
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Cite This
Bradley Efron,
Robert Tibshirani
(2002).
Empirical bayes methods and false discovery rates for microarrays.
Genetic Epidemiology
, 23
(1)
, 70-86.
https://doi.org/10.1002/gepi.1124
Identifiers
- DOI
- 10.1002/gepi.1124