Bayesian point estimation and predictive density estimation for the binomial distribution with a restricted probability parameter

被引：1

作者：

Hamura, Yasuyuki ^{[1
]}

机构：

[1] Univ Tokyo, Grad Sch Econ, Bunkyo Ku, 7-3-1 Hongo, Tokyo 1130033, Japan

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2023年 / 52卷 / 11期

基金：

日本学术振兴会;

关键词：

Bayesian point estimation; Bayesian predictive density estimation; binomial distribution; dominance; Kullback-Leibler divergence; restricted parameter space; EXPONENTIAL-DISTRIBUTION; SPACE;

D O I：

10.1080/03610926.2021.1980046

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：

In this paper, we consider Bayesian point estimation and predictive density estimation in the binomial case. After presenting preliminary results on these problems, we compare the risk functions of the Bayes estimators based on the truncated and untruncated beta priors and obtain dominance conditions when the probability parameter is less than or equal to a known constant. The case where there are both a lower bound restriction and an upper bound restriction is also treated. Then our problems are shown to be related to similar problems in the Poisson case. Finally, numerical studies are presented.

引用

页码：3767 / 3794

页数：28