Identifying leading measurement units from a large collection is a common inference task in various domains of large-scale inference. Testing approaches, which measure evidence against a null hypothesis rather than effect magnitude, tend to overpopulate lists of leading units with those associated with low measurement error. By contrast, local maximum likelihood (ML) approaches tend to favor units with high measurement error. Available Bayesian and empirical Bayesian approaches rely on specialized loss functions that result in similar deficiencies. We describe and evaluate a generic empirical Bayesian ranking procedure that populates the list of top units in a way that maximizes the expected overlap between the true and reported top lists for all list sizes. The procedure relates unit-specific posterior upper tail probabilities with their empirical distribution to yield a ranking variable. It discounts high-variance units less than popular non-ML methods and thus achieves improved operating characteristics in the models considered.

中文翻译：

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切入点：改进了用于大规模推理的排名和选择。

在大型推理的各个领域中，从大型集合中识别出领先的测量单位是一项常见的推理任务。测试方法是针对无效假设而不是效应量度的证据，倾向于用与低测量误差相关的方法来填充主要单元的列表。相比之下，局部最大似然（ML）方法倾向于偏爱具有高测量误差的单元。可用的贝叶斯方法和经验贝叶斯方法依赖于导致相似缺陷的专门损失函数。我们描述并评估了通用的经验贝叶斯排名程序，该程序以使所有列表大小的真实和已报告顶部列表之间的预期重叠最大化的方式填充顶部单元列表。该过程将特定于单元的后上尾概率与其经验分布相关联，以产生等级变量。与常用的非机器学习方法相比，它对高方差单元的折价要低，因此可以在所考虑的模型中实现改进的运行特性。