Empirical Bayes When Estimation Precision Predicts Parameters
提出一种灵活参数化的位置-尺度分布族（close方法），用于建模参数与标准误差之间的依赖关系，在经验贝叶斯框架下统一并推广了多种精度依赖方法，实证表明在选取高流动性人口普查区时效果显著。
Gaussian empirical Bayes methods usually maintain a precision independence assumption: The unknown parameters of interest are independent from the known standard errors of the estimates. This assumption is often theoretically questionable and empirically rejected. This paper proposes to model the conditional distribution of the parameter given the standard errors as a flexibly parameterized location‐scale family of distributions, leading to a family of methods that we call close . The close framework unifies and generalizes several proposals under precision dependence. We argue that the most flexible member of the close family is a minimalist and computationally efficient default for accounting for precision dependence. We analyze this method and show that it is competitive in terms of the regret of subsequent decision rules. Empirically, using close leads to sizable gains for selecting high‐mobility Census tracts.