文章基本信息

标题：Optimal false discovery rate control for dependent data
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作者：T. Tony Cai ; Hongzhe Li ; John Maris 等
期刊名称：Statistics and Its Interface
印刷版ISSN：1938-7989
电子版ISSN：1938-7997
出版年度：2011
卷号：4
期号：4
页码：417-430
DOI：10.4310/SII.2011.v4.n4.a1
出版社：International Press
摘要：This paper considers the problem of optimal false discovery rate control when the test statistics are dependent. An optimal joint oracle procedure, which minimizes the false non-discovery rate subject to a constraint on the false discovery rate is developed. A data-driven marginal plug-in procedure is then proposed to approximate the optimal joint procedure for multivariate normal data. It is shown that the marginal procedure is asymptotically optimal for multivariate normal data with a short-range dependent covariance structure. Numerical results show that the marginal procedure controls false discovery rate and leads to a smaller false non-discovery rate than several commonly used $p$-value based false discovery rate controlling methods. The procedure is illustrated by an application to a genome-wide association study of neuroblastoma and it identifies a few more genetic variants that are potentially associated with neuroblastoma than several $p$-value-based false discovery rate controlling procedures.
关键词：large scale multiple testing; marginal rule; optimal oracle rule; weighted classification