结合可能相关的估计问题

B. Efron, C. Morris
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引用次数: 155

摘要

我们有两组参数需要估计,并且想知道是否应该将James-Stein估计器分别应用于这两组参数还是一次应用于组合问题。我们证明了存在一类折衷估计器,本质上是贝叶斯估计器,它通常优于任意一种选择。“这里的困难是知道什么问题要组合在一起——为什么我们所有的估计问题不应该集中在一起成为一个大混战?”GEORGE BARNARD评论James-Stein估计器,1962。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combining Possibly Related Estimation Problems
SUMMARY We have two sets of parameters we wish to estimate, and wonder whether the James-Stein estimator should be applied separately to the two sets or once to the combined problem. We show that there is a class of compromise estimators, Bayesian in nature, which will usually be preferred to either alternative. "The difficulty here is to know what problems are to be combined togetherwhy should not all our estimation problems be lumped together into one grand melee ?" GEORGE BARNARD commenting on the James-Stein estimator, 1962.
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