A class of proper priors for Bayesian simultaneous prediction of independent Poisson observables

被引：12

作者：

Komaki, Fumiyasu ^{[1
]}

机构：

[1] Univ Tokyo, Dept Math Informat, Grad Sch Informat Sci & Technol, Bunkyo Ku, Tokyo 1138656, Japan

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2006年 / 97卷 / 08期

关键词：

Jeffreys prior; Kullback-Leibler divergence; predictive density; shrinkage prior;

D O I：

10.1016/j.jmva.2005.12.002

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：

Simultaneous prediction and parameter inference for the independent Poisson observables model are considered. A class of proper prior distributions for Poisson means is introduced. Bayesian predictive densities and estimators based on priors in the introduced class dominate the Bayesian predictive density and estimator based on the Jeffreys prior under Kullback-Leibler loss. (c) 2005 Elsevier Inc. All rights reserved.

引用

页码：1815 / 1828

页数：14

共 9 条

[1]

GEORGE EI, 2006, IN PRESS ANN STAT, V34

[2] SIMULTANEOUS ESTIMATION OF POISSON MEANS UNDER ENTROPY LOSS [J].