A BAYESIAN PREDICTIVE APPROACH TO DETERMINING THE NUMBER OF COMPONENTS IN A MIXTURE DISTRIBUTION

被引：22

作者：

DEY, DK

KUO, L

SAHU, SK

机构：

[1] UNIV CONNECTICUT,DEPT STAT,STORRS,CT 06269

[2] UNIV CAMBRIDGE,STAT LAB,CAMBRIDGE,ENGLAND

来源：

STATISTICS AND COMPUTING | 1995年 / 5卷 / 04期

关键词：

BOOTSTRAP PROCEDURES; CONDITIONAL PREDICTIVE ORDINATE; GAMMA MIXTURES; GIBBS SAMPLER; LIKELIHOOD RATIO (LR) STATISTIC; METROPOLIS ALGORITHM; MONTE CARLO METHODS; NORMAL MIXTURES; PREDICTIVE DISTRIBUTION; PSEUDO BAYES FACTOR;

D O I：

10.1007/BF00162502

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper describes a Bayesian approach to mixture modelling and a method based on predictive distribution to determine the number of components in the mixtures. The implementation is done through the use of the Gibbs sampler. The method is described through the mixtures of normal and gamma distributions. Analysis is presented in one simulated and one real data example. The Bayesian results are then compared with the likelihood approach for the two examples.

引用

页码：297 / 305

页数：9

共 28 条

[1]

Berger J.O., 1985, STAT DECISION THEORY, P74

[2] SAMPLING AND BAYES INFERENCE IN SCIENTIFIC MODELING AND ROBUSTNESS [J].