Bayesian Multiple Testing Under Sparsity for Polynomial-Tailed Distributions

Abstract

This paper considers Bayesian multiple testing under sparsity for polynomial-tailed distributions satisfying a monotone likelihood ratio property. Included in this class of distributions are the Student's t, the Pareto, and many other distributions. We prove some general asymptotic optimality results under fixed and random thresholding. As examples of these general results, we establish the Bayesian asymptotic optimality of several multiple testing procedures in the literature for appropriately chosen false discovery rate levels. We also show by simulation that the Benjamini-Hochberg procedure with a false discovery rate level different from the asymptotically optimal one can lead to high Bayes risk.

27 pages, 4 figures, 1 table

References in corpus (4)

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• A comparison of the Benjamini-Hochberg procedure with some Bayesian rules for multiple testing
• Asymptotic minimaxity of False Discovery Rate thresholding for sparse exponential data
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