On false discovery control under dependence

Abstract

A popular framework for false discovery control is the random effects model in which the null hypotheses are assumed to be independent. This paper generalizes the random effects model to a conditional dependence model which allows dependence between null hypotheses. The dependence can be useful to characterize the spatial structure of the null hypotheses. Asymptotic properties of false discovery proportions and numbers of rejected hypotheses are explored and a large-sample distributional theory is obtained.

Published in at http://dx.doi.org/10.1214/009053607000000730 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (2)

• A stochastic process approach to false discovery control
• Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses

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