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March 2008 Simultaneous inference: When should hypothesis testing problems be combined?
Bradley Efron
Ann. Appl. Stat. 2(1): 197-223 (March 2008). DOI: 10.1214/07-AOAS141


Modern statisticians are often presented with hundreds or thousands of hypothesis testing problems to evaluate at the same time, generated from new scientific technologies such as microarrays, medical and satellite imaging devices, or flow cytometry counters. The relevant statistical literature tends to begin with the tacit assumption that a single combined analysis, for instance, a False Discovery Rate assessment, should be applied to the entire set of problems at hand. This can be a dangerous assumption, as the examples in the paper show, leading to overly conservative or overly liberal conclusions within any particular subclass of the cases. A simple Bayesian theory yields a succinct description of the effects of separation or combination on false discovery rate analyses. The theory allows efficient testing within small subclasses, and has applications to “enrichment,” the detection of multi-case effects.


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Bradley Efron. "Simultaneous inference: When should hypothesis testing problems be combined?." Ann. Appl. Stat. 2 (1) 197 - 223, March 2008.


Published: March 2008
First available in Project Euclid: 24 March 2008

zbMATH: 1137.62010
MathSciNet: MR2415600
Digital Object Identifier: 10.1214/07-AOAS141

Keywords: enrichment , False discovery rates , separate-class model

Rights: Copyright © 2008 Institute of Mathematical Statistics

Vol.2 • No. 1 • March 2008
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