Introduces NSBM and a VEM-plus-FDR procedure that controls false discovery rate for graph inference with optimal true discovery rate up to small remainder terms as graph size grows.
On spike and slab empirical Bayes multiple testing
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abstract
This paper explores a connection between empirical Bayes posterior distributions and false discovery rate (FDR) control. In the Gaussian sequence model, this work shows that empirical Bayes-calibrated spike and slab posterior distributions allow a correct FDR control under sparsity. Doing so, it offers a frequentist theoretical validation of empirical Bayes methods in the context of multiple testing. Our theoretical results are illustrated with numerical experiments.
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math.ST 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Graph inference with clustering and false discovery rate control
Introduces NSBM and a VEM-plus-FDR procedure that controls false discovery rate for graph inference with optimal true discovery rate up to small remainder terms as graph size grows.