Model‐based clustering of longitudinal data |
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Authors: | Paul D. McNicholas T. Brendan Murphy |
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Affiliation: | 1. Department of Mathematics & Statistics, University of Guelph, Guelph, Ontario, Canada N1G 2W1;2. School of Mathematical Sciences, University College Dublin, Belfield, Dublin 4, Ireland |
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Abstract: | A new family of mixture models for the model‐based clustering of longitudinal data is introduced. The covariance structures of eight members of this new family of models are given and the associated maximum likelihood estimates for the parameters are derived via expectation–maximization (EM) algorithms. The Bayesian information criterion is used for model selection and a convergence criterion based on the Aitken acceleration is used to determine the convergence of these EM algorithms. This new family of models is applied to yeast sporulation time course data, where the models give good clustering performance. Further constraints are then imposed on the decomposition to allow a deeper investigation of the correlation structure of the yeast data. These constraints greatly extend this new family of models, with the addition of many parsimonious models. The Canadian Journal of Statistics 38:153–168; 2010 © 2010 Statistical Society of Canada |
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Keywords: | Cholesky decomposition longitudinal data mixture models model‐based clustering time course data yeast sporulation MSC 2000: Primary 62H30 secondary 62P10 |
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