|
|||||
|
|
A Bayesian predictive approach to determining the number of components in a mixture distribution |
|
| Authors: | Dipak K Dey Lynn Kuo Sujit K Sahu |
| Institution: | (1) Department of Statistics, University of Connecticut, 06269-3120 Storrs, CT, USA;(2) Statistical Laboratory, University of Cambridge, Cambridge, UK |
| Abstract: | 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. |
| Keywords: | 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 |
| 本文献已被 SpringerLink 等数据库收录！ | |