Group for Research in Decision Analysis

Model-Based Clustering of Longitudinal Data

Paul Mcnicholas

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 estimated using expectation-maximization (EM) algorithms. The Bayesian information criterion is used for model selection and Aitken's acceleration is used to determine convergence of these EM algorithms. This family of models is then applied to two toy data sets and to to the famous yeast sporulation time course data of Chu et al., where the models display good clustering performance. Finally, further constraints are imposed on the decomposition to allow a deeper investigation of correlation structure of these yeast sporulation data.