论文标题
经验贝叶斯当估计精度预测参数时
Empirical Bayes When Estimation Precision Predicts Parameters
论文作者
论文摘要
高斯经验贝叶斯方法通常保持精确的独立性假设：未知参数与估计值的已知标准误差无关。这种假设通常在理论上是值得怀疑的，并且在经验上被拒绝。本文提议将参数的条件分布建模为标准误差作为灵活的参数位置尺度分布家族，从而导致我们称之为关闭的方法家族。近距离框架在精确依赖性下统一并概括了几项建议。我们认为，亲密家族中最灵活的成员是极简主义和计算有效的默认成员，以考虑精确依赖性。我们分析了这种方法，并表明它在后续决策规则的遗憾方面具有竞争力。从经验上讲，使用近距离导致选择高弹性人口普查区域的可观收益。
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 parametrized 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 decisions rules. Empirically, using CLOSE leads to sizable gains for selecting high-mobility Census tracts.
