Bayesian Multiple Change-Point Estimation of Multivariate Mean Vectors for Small Data

被引：0
|
作者：
Cheon, Sooyoung [1 ]
Yu, Wenxing [2 ]
机构：
[1] Korea Univ, Dept Informat Stat, 2511 Sejong Ro, Sejong City 339700, South Korea
[2] Korea Univ, Dept Econ & Stat, Seoul, South Korea
来源：
KOREAN JOURNAL OF APPLIED STATISTICS | 2012年 / 25卷 / 06期
关键词：
Small data; change-point; noncentral t-distribution; Metropolis-Hastings-Within-Gibbs sampling; Hanwoo fat content;
D O I：
10.5351/KJAS.2012.25.6.999
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：
A Bayesian multiple change-point model for small data is proposed for multivariate means and is an extension of the univariate case of Cheon and Yu (2012). The proposed model requires data from a multivariate noncentral t-distribution and conjugate priors for the distributional parameters. We apply the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model to detecte multiple change-points. The performance of our proposed algorithm has been investigated on simulated and real dataset, Hanwoo fat content bivariate data.
引用
收藏
页码：999 / 1008
页数：10
相关论文
共 50 条
12345