title: Optimal False Discovery Rate Control for Dependent Data.
abstract: 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.
authors: Department of Statistics, The Fox School of Business and Management, Temple University,  jichun@temple.edu.
authors: Jichun
authors: J
authors: Xie
authors: T Tony
authors: TT
authors: Cai
authors: John
authors: J
authors: Maris
authors: Hongzhe
authors: H
authors: Li
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