Proposes FDR-controlling posterior decision rules for signal detection under horseshoe and similar continuous shrinkage priors that attain the optimal detection boundary with asymptotic FDR and FNR control in sparse normal means models.
andVAN DERVAART, A
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Predictively consistent priors let complex Bayesian models match or beat the out-of-sample performance of selected simpler models across linear, logistic, and nonlinear examples without explicit selection.
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Multiple testing with the horseshoe
Proposes FDR-controlling posterior decision rules for signal detection under horseshoe and similar continuous shrinkage priors that attain the optimal detection boundary with asymptotic FDR and FNR control in sparse normal means models.