A bayesian predictive approach to determining the number of components in a mixture distribution


Dey, D.K., Kuo, L. and Sahu, Sujit K. (1995) A bayesian predictive approach to determining the number of components in a mixture distribution. Statistics and Computing, 5, (4), 297-305. (doi:10.1007/BF00162502).

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Original Publication URL: http://dx.doi.org/10.1007/BF00162502

Description/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.

Item Type: Article
ISSNs: 0960-3174 (print)
Related URLs:
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
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Divisions: University Structure - Pre August 2011 > School of Mathematics > Statistics
ePrint ID: 54073
Date Deposited: 05 Aug 2008
Last Modified: 27 Mar 2014 18:37
URI: http://eprints.soton.ac.uk/id/eprint/54073

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