Show simple item record McNicholas, Paul D. Murphy, Thomas Brendan 2011-03-10T11:25:35Z 2011-03-10T11:25:35Z 2010 Statistical Society of Canada en 2010-03
dc.identifier.citation Canadian Journal of Statistics en
dc.identifier.issn 1708-945X
dc.description.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 Aitken’s acceleration is used to determine 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 correlation structure of the yeast data. These constraints greatly extend this new family of models, with the addition of many parsimonious models. en
dc.description.sponsorship Higher Education Authority en
dc.format.extent 244478 bytes
dc.format.mimetype application/pdf
dc.language.iso en en
dc.publisher Wiley en
dc.rights This is the author's version of the following article: "Model-based clustering of longitudinal data" published in The Canadian Journal of Statistics Vol. 34, No. 4, 2006, available at en
dc.subject Cholesky decomposition en
dc.subject Longitudinal data en
dc.subject Mixture models en
dc.subject Model-based clustering en
dc.subject Time course data en
dc.subject Yeast sporulation en
dc.subject.lcsh Decomposition method en
dc.subject.lcsh Longitudinal method--Mathematical models en
dc.subject.lcsh Mixture distributions (Probability theory) en
dc.subject.lcsh Cluster analysis en
dc.subject.lcsh Yeast--Growth--Mathematics en
dc.title Model-based clustering of longitudinal data en
dc.type Journal Article en
dc.internal.availability Full text available en
dc.internal.webversions en
dc.status Peer reviewed en
dc.identifier.volume 38 en
dc.identifier.issue 1 en
dc.identifier.startpage 153 en
dc.identifier.endpage 168 en
dc.identifier.doi 10.1002/cjs.10047
dc.neeo.contributor McNicholas|Paul D.|aut| en
dc.neeo.contributor Murphy|Thomas Brendan|aut| en

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