Model-based clustering of longitudinal data
|Title:||Model-based clustering of longitudinal data||Authors:||McNicholas, Paul D.
Murphy, Thomas Brendan
|Permanent link:||http://hdl.handle.net/10197/2834||Date:||Mar-2010||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.||Funding Details:||Higher Education Authority||Type of material:||Journal Article||Publisher:||Wiley||Copyright (published version):||2010 Statistical Society of Canada||Keywords:||Cholesky decomposition; Longitudinal data; Mixture models; Model-based clustering; Time course data; Yeast sporulation||Subject LCSH:||Decomposition method
Longitudinal method--Mathematical models
Mixture distributions (Probability theory)
|DOI:||10.1002/cjs.10047||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Mathematics and Statistics Research Collection|
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