Model‐based clustering of longitudinal data
Copyright policy )Model‐based clustering of longitudinal data
AbstractA 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
- University College Dublin Ireland
- University of Guelph Canada
Classification and discrimination; cluster analysis (statistical aspects), Longitudinal data, Estimation in multivariate analysis, Computational problems in statistics, 310, Yeast sporulation, Applications of statistics to biology and medical sciences; meta analysis, Model-based clustering, Time course data, Cluster analysis, Longitudinal method--Mathematical models, yeast sporulation, time course data, Decomposition method, mixture models, Mixture models, Mixture distributions (Probability theory), Yeast--Growth--Mathematics, Cholesky decomposition
Classification and discrimination; cluster analysis (statistical aspects), Longitudinal data, Estimation in multivariate analysis, Computational problems in statistics, 310, Yeast sporulation, Applications of statistics to biology and medical sciences; meta analysis, Model-based clustering, Time course data, Cluster analysis, Longitudinal method--Mathematical models, yeast sporulation, time course data, Decomposition method, mixture models, Mixture models, Mixture distributions (Probability theory), Yeast--Growth--Mathematics, Cholesky decomposition
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