Optimal False Discovery Rate Control for Dependent Data.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Xie, J; Cai, TT; Maris, J; Li, H

Published Date

  • 2011

Published In

Volume / Issue

  • 4 / 4

Start / End Page

  • 417 - 430

PubMed ID

  • 23378870

Pubmed Central ID

  • PMC3559028

International Standard Serial Number (ISSN)

  • 1938-7989

Digital Object Identifier (DOI)

  • 10.4310/sii.2011.v4.n4.a1


  • eng

Conference Location

  • United States