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Mixture Models

Mixture models experience sustainable popularity over recent years. Not only that they are natural models to adjust for unobserved or latent heterogeneity, they are fundamental cornerstones in many areas in statistics such as smoothing, empirical Bayes, likelihood based clustering, or latent variable analysis among others. As semi-parametric models they combine par excellence the compromise in the trade-off between imposed model structure and freedom in model adaptation to the data. However, mixture models experience a number of difficulties. The likelihood may not be bounded, and, even if it were, the global maximum might not be a good choice. Algorithmic solutions are nearly almost required and algorithms such as the EM algoirthm is experiencing numerous problems such as the choice of initial values or using an adequate stopping rule. The number of components problem and model selection add one more to the many areas of interest. Diverse application areas such as capture-rapture approaches or clustering of gene expression data have been added to numerous existing application areas such as disease mapping or meta-analysis. The track is primarily devoted to these newly emerging issues.

Geoff McLachlan, University of Queensland, Australia.
Paul McNicholas, University of Guelph, Canada.
Organized Sessions associated with this Track