A nonparametric empirical Bayes framework for large-scale multiple testing

Details A nonparametric empirical Bayes framework for large-scale multiple testing A nonparametric empirical Bayes framework for large-scale multiple testing

2011-06-20

arxiv.org/abs/1106.3885v3

Statistics

Methodology

18 pages, 4 figures, 3 tables

Scientific paper

We propose a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-null cases. We use a computationally efficient predictive recursion 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 non-null density can give a much better fit in the tails of the mixture distribution which, in turn, can lead to more realistic conclusions.

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