Model-based clustering of longitudinal data

DC FieldValueLanguage
dc.contributor.authorMcNicholas, Paul D.-
dc.contributor.authorMurphy, Thomas Brendan-
dc.date.accessioned2011-03-10T11:25:35Z-
dc.date.available2011-03-10T11:25:35Z-
dc.date.copyright2010 Statistical Society of Canadaen
dc.date.issued2010-03-
dc.identifier.citationCanadian Journal of Statisticsen
dc.identifier.issn1708-945X-
dc.identifier.urihttp://hdl.handle.net/10197/2834-
dc.description.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 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.sponsorshipHigher Education Authorityen
dc.format.extent244478 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.publisherWileyen
dc.rightsThis 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 http://dx.doi.org/10.1002/cjs.10047en
dc.subjectCholesky decompositionen
dc.subjectLongitudinal dataen
dc.subjectMixture modelsen
dc.subjectModel-based clusteringen
dc.subjectTime course dataen
dc.subjectYeast sporulationen
dc.subject.lcshDecomposition methoden
dc.subject.lcshLongitudinal method--Mathematical modelsen
dc.subject.lcshMixture distributions (Probability theory)en
dc.subject.lcshCluster analysisen
dc.subject.lcshYeast--Growth--Mathematicsen
dc.titleModel-based clustering of longitudinal dataen
dc.typeJournal Articleen
dc.internal.availabilityFull text availableen
dc.internal.webversionshttp://dx.doi.org/10.1002/cjs.10047-
dc.statusPeer revieweden
dc.identifier.volume38en
dc.identifier.issue1en
dc.identifier.startpage153en
dc.identifier.endpage168en
dc.identifier.doi10.1002/cjs.10047-
dc.neeo.contributorMcNicholas|Paul D.|aut|-
dc.neeo.contributorMurphy|Thomas Brendan|aut|-
item.grantfulltextopen-
item.fulltextWith Fulltext-
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