Optimal post-selection inference for sparse signals: a nonparametric empirical Bayes approach
稀疏信号的最优选择后推断：非参数经验贝叶斯方法
Summary Many recently developed Bayesian methods focus on sparse signal detection. However, much less work has been done on the natural follow-up question: how does one make valid inferences for the magnitude of those signals after selection? Ordinary Bayesian credible intervals suffer from selection bias, as do ordinary frequentist confidence intervals. Existing Bayesian methods for correcting this bias produce credible intervals with poor frequentist properties. Further,…