Model-based clustering via linear cluster-weighted models

A novel family of twelve mixture models with random covariates, nested in the linear \(t\) cluster-weighted model (CWM), is introduced for model-based clustering. The linear \(t\) CWM was recently presented as a robust alternative to the better known linear Gaussian CWM. The proposed family of model...

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Bibliographic Details
Published in:arXiv.org 2015-03
Main Authors: Ingrassia, Salvatore, Minotti, Simona C, Punzo, Antonio
Format: Article
Language:English
Subjects:
Algorithms
Bayesian analysis
Clustering
Computer simulation
Maximum likelihood estimation
Parameter estimation
Probabilistic models
Online Access:Get full text
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Summary:A novel family of twelve mixture models with random covariates, nested in the linear \(t\) cluster-weighted model (CWM), is introduced for model-based clustering. The linear \(t\) 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.
ISSN:2331-8422
DOI:10.48550/arxiv.1206.3974