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February 2008 Microarrays, Empirical Bayes and the Two-Groups Model
Bradley Efron
Statist. Sci. 23(1): 1-22 (February 2008). DOI: 10.1214/07-STS236

Abstract

The classic frequentist theory of hypothesis testing developed by Neyman, Pearson and Fisher has a claim to being the twentieth century’s most influential piece of applied mathematics. Something new is happening in the twenty-first century: high-throughput devices, such as microarrays, routinely require simultaneous hypothesis tests for thousands of individual cases, not at all what the classical theory had in mind. In these situations empirical Bayes information begins to force itself upon frequentists and Bayesians alike. The two-groups model is a simple Bayesian construction that facilitates empirical Bayes analysis. This article concerns the interplay of Bayesian and frequentist ideas in the two-groups setting, with particular attention focused on Benjamini and Hochberg’s False Discovery Rate method. Topics include the choice and meaning of the null hypothesis in large-scale testing situations, power considerations, the limitations of permutation methods, significance testing for groups of cases (such as pathways in microarray studies), correlation effects, multiple confidence intervals and Bayesian competitors to the two-groups model.

Citation

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Bradley Efron. "Microarrays, Empirical Bayes and the Two-Groups Model." Statist. Sci. 23 (1) 1 - 22, February 2008. https://doi.org/10.1214/07-STS236

Information

Published: February 2008
First available in Project Euclid: 7 July 2008

zbMATH: 1327.62047
MathSciNet: MR2431866
Digital Object Identifier: 10.1214/07-STS236

Rights: Copyright © 2008 Institute of Mathematical Statistics

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Vol.23 • No. 1 • February 2008
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