Bayesian prediction based on a class of shrinkage priors for location-scale models

被引：0

作者：

Fumiyasu Komaki

机构：

[1] The University of Tokyo,Department of Mathematical Informatics, Graduate School of Information Science and Technology

来源：

Annals of the Institute of Statistical Mathematics | 2007年 / 59卷

关键词：

Asymptotic theory; Jeffreys prior; Neyman–Scott model; Right invariant prior; Kullback–Leibler divergence;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A class of shrinkage priors for multivariate location-scale models is introduced. We consider Bayesian predictive densities for location-scale models and evaluate performance of them using the Kullback–Leibler divergence. We show that Bayesian predictive densities based on priors in the introduced class asymptotically dominate the best invariant predictive density.

引用

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页码：135 / 146

页数：11

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