A note concerning a selection "Paradox" of Dawid's

被引：35

作者：

Senn, Stephen ^{[1
]}

机构：

[1] Univ Glasgow, Dept Stat, Glasgow G12 8QQ, Lanark, Scotland

来源：

AMERICAN STATISTICIAN | 2008年 / 62卷 / 03期

基金：

英国工程与自然科学研究理事会;

关键词：

Bayesian inference; hierarchical models; prior distributions; selection paradox;

D O I：

10.1198/000313008X331530

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This article briefly reviews a selection "paradox" of Dawid's, whereby Bayesian inference appears to be unchanged whether or not treatments have been selected for inspection on the basis of extreme values. The problem is recast in terms of a hierarchical model. This offers an alternative explanation of the paradox but also reveals a disturbing dependence of inference on prior specification. The example may also be used to deepen students' understanding of the implications of using conjugate nonhierarchical priors in Bayesian analysis. To illustrate, some simulations are presented.

引用

页码：206 / 210

页数：5

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