A nonparametric empirical Bayes framework for large-scale multiple testing

Journal Article

We propose a flexible and identifiable version of the 2-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the nonnull cases. We use a computationally efficient predictive recursion (PR) marginal likelihood procedure to estimate the model parameters, even the nonparametric mixing distribution. This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates. Simulations and real data examples demonstrate that, compared to existing approaches, PRtest's careful handling of the nonnull density can give a much better fit in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions. The Author 2011. Published by Oxford University Press. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Martin, R; Tokdar, ST

Published Date

  • 2012

Published In

Volume / Issue

  • 13 / 3

Start / End Page

  • 427 - 439

PubMed ID

  • 22085895

Pubmed Central ID

  • 22085895

International Standard Serial Number (ISSN)

  • 1465-4644

Digital Object Identifier (DOI)

  • 10.1093/biostatistics/kxr039