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).
Full text not available from this repository.
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|
|Subjects:||Q Science > Q Science (General)
Q Science > QA Mathematics
|Divisions:||University Structure - Pre August 2011 > School of Mathematics > Statistics
|Date Deposited:||05 Aug 2008|
|Last Modified:||01 Jun 2011 04:50|
|Contributors:||Dey, D.K. (Author)
Kuo, L. (Author)
Sahu, Sujit K. (Author)
|Contact Email Address:||S.K.Sahu@soton.ac.uk|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
Actions (login required)