A nonparametric empirical Bayes framework for large-scale multiple testing
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.
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Related Subject Headings
- Statistics & Probability
- Oligonucleotide Array Sequence Analysis
- Models, Statistical
- Likelihood Functions
- Leukemia
- Humans
- Female
- Data Interpretation, Statistical
- Computer Simulation
- Breast Neoplasms
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Statistics & Probability
- Oligonucleotide Array Sequence Analysis
- Models, Statistical
- Likelihood Functions
- Leukemia
- Humans
- Female
- Data Interpretation, Statistical
- Computer Simulation
- Breast Neoplasms