Oskrochi, Gholam and Davies, R. B. (1997) An EM-type algorithm for multivariate mixture models. Statistics and Computing, 7 (2). pp. 145-151. ISSN 0960-3174Full text not available from this repository.
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.
|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|
|Deposited On:||10 Nov 2008 11:59|
|Last Modified:||25 Mar 2017 01:19|
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