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Large-scale multiple testing under dependence (2009)
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| Venue: | J ROY STAT SOC B |
| Citations: | 25 - 2 self |
BibTeX
@ARTICLE{Sun09large-scalemultiple,
author = {Wenguang Sun and T. Tony Cai},
title = {Large-scale multiple testing under dependence},
journal = {J ROY STAT SOC B},
year = {2009},
pages = {393--424}
}
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Abstract
Summary. The paper considers the problem of multiple testing under dependence in a compound decision theoretic framework. The observed data are assumed to be generated from an underlying two-state hidden Markov model.We propose oracle and asymptotically optimal datadriven procedures that aim to minimize the false non-discovery rate FNR subject to a constraint on the false discovery rate FDR. It is shown that the performance of a multiple-testing procedure can be substantially improved by adaptively exploiting the dependence structure among hypotheses, and hence conventional FDR procedures that ignore this structural information are inefficient. Both theoretical properties and numerical performances of the procedures proposed are investigated. It is shown that the procedures proposed control FDR at the desired level, enjoy certain optimality properties and are especially powerful in identifying clustered non-null cases. The new procedure is applied to an influenza-like illness surveillance study for detecting the timing of epidemic periods.
Keyphrases
two-state hidden markov model false discovery rate fdr conventional fdr procedure structural information epidemic period false non-discovery rate fnr subject observed data desired level compound decision theoretic framework control fdr theoretical property influenza-like illness surveillance study optimal datadriven procedure dependence structure new procedure non-null case numerical performance certain optimality property multiple-testing procedure
