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

页码：999 / 1008

页数：10

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