Approximating Bayesian inference by weighted likelihood

被引：14

作者：

Wang, Xiaogang ^{[1
]}

机构：

[1] York Univ, Dept Math & Stat, N York, ON M3J 1P3, Canada

来源：

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE | 2006年 / 34卷 / 02期

关键词：

empirical Bayes; entropy loss; hierarchical Bayes; James-Stein estimator; nonparametric regression; weighted likelihood;

D O I：

10.1002/cjs.5550340206

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：

The author proposes to use weighted likelihood to approximate Bayesian inference when no external or prior information is available. He proposes a weighted likelihood estimator that minimizes the empirical Bayes risk under relative entropy loss. He discusses connections among the weighted likelihood, empirical Bayes and James-Stein estimators. Both simulated and real data sets are used for illustration purposes.

引用

页码：279 / 298

页数：20

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