The Annals of Applied Statistics

Simultaneous inference: When should hypothesis testing problems be combined?

Bradley Efron

Full-text: Open access

Abstract

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.

Article information

Source
Ann. Appl. Stat. Volume 2, Number 1 (2008), 197-223.

Dates
First available in Project Euclid: 24 March 2008

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1206367818

Digital Object Identifier
doi:10.1214/07-AOAS141

Mathematical Reviews number (MathSciNet)
MR2415600

Zentralblatt MATH identifier
1137.62010

Keywords
False discovery rates separate-class model enrichment

Citation

Efron, Bradley. Simultaneous inference: When should hypothesis testing problems be combined?. Ann. Appl. Stat. 2 (2008), no. 1, 197--223. doi:10.1214/07-AOAS141. https://projecteuclid.org/euclid.aoas/1206367818.


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