1. Model-based clustering via linear cluster-weighted models.
- Author
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Ingrassia, Salvatore, Minotti, Simona C., and Punzo, Antonio
- Subjects
- *
CLUSTER analysis (Statistics) , *LINEAR statistical models , *STATISTICAL models , *RANDOM effects model , *GAUSSIAN mixture models , *PARAMETER estimation - Abstract
Abstract: A novel family of twelve mixture models with random covariates, nested in the linear cluster-weighted model (CWM), is introduced for model-based clustering. The linear CWM was recently presented as a robust alternative to the better known linear Gaussian CWM. The proposed family of models provides a unified framework that also includes the linear Gaussian CWM as a special case. Maximum likelihood parameter estimation is carried out within the EM framework, and both the BIC and the ICL are used for model selection. A simple and effective hierarchical–random initialization is also proposed for the EM algorithm. The novel model-based clustering technique is illustrated in some applications to real data. Finally, a simulation study for evaluating the performance of the BIC and the ICL is presented. [Copyright &y& Elsevier]
- Published
- 2014
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