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An EM-type algorithm for multivariate mixture models.

Oskrochi, Gholam and Davies, R. B. (1997) An EM-type algorithm for multivariate mixture models. Statistics and Computing, 7 (2). pp. 145-151. ISSN 0960-3174

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Abstract

This paper introduces a new approach, based on dependent univariate GLMs, for fitting multivariate mixture models. This approach is a multivariate generalization of the method for univariate mixtures presented by Hinde (1982). Its accuracy and efficiency are compared with direct maximization of the log-likelihood. Using a simulation study, we also compare the efficiency of Monte Carlo and Gaussian quadrature methods for approximating the mixture distribution. The new approach with Gaussian quadrature outperforms the alternative methods considered. The work is motivated by the multivariate mixture models which have been proposed for modelling changes of employment states at an individual level. Similar formulations are of interest for modelling movement between other social and economic states and multivariate mixture models also occur in biostatistics and epidemiology.

Item Type: Article
Journal or Publication Title: Statistics and Computing
Uncontrolled Keywords: Multivariate generalized linear models - Markov model - EM algorithm - random effect models - Monte Carlo simulation - Cholesky decomposition
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 19692
Deposited By: ep_ss_importer
Deposited On: 10 Nov 2008 11:59
Refereed?: Yes
Published?: Published
Last Modified: 26 Jul 2012 15:34
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/19692

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