In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior. In this paper, we develop flexible and practical confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands, such as the posterior mean or the local false sign rate. The coverage statements hold even when the estimands are only partially identified or when empirical Bayes point estimates converge very slowly.

翻译：在一项经验性贝耶斯分析中,我们利用反复抽样的数据来模仿对数据生成分布有广泛知识的贝耶斯先知所作的推论;现有结果提供了对经验性贝耶斯点准确恢复贝耶斯山的行为的时间和原因的全面描述;在本文中,我们制定了灵活而实际的信任间隔,对经验性贝耶斯山地平原,例如后方平均值或当地假信号率等提供无症状性常客覆盖。即使仅部分地确定海神座或经验性贝亚斯山地平面估计非常缓慢地汇合时,覆盖也仍然有效。