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Statistics and Computing

, Volume 7, Issue 2, pp 145–151 | Cite as

An EM-type algorithm for multivariate mixture models

  • G. R. Oskrochi
  • R. B. Davies
Article
  • 134 Downloads

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.

Multivariate generalized linear models Markov model EM algorithm random effect models Monte Carlo simulation Cholesky decomposition 

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Copyright information

© Chapman and Hall 1997

Authors and Affiliations

  • G. R. Oskrochi
    • 1
  • R. B. Davies
    • 1
  1. 1.Centre for Applied StatisticsLancaster UniversityLancasterUK

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