Antonio Punzo

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 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.

Clustering and classification via cluster-weighted factor analyzers

In model-based clustering and classification, the cluster-weighted model is a convenient approach when the random vector of interest is constituted by a response variable

Cluster-weighted \(t\)-factor analyzers for robust model-based clustering and dimension reduction

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The Autodependogram: A Graphical Device to Investigate Serial Dependences

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Clustering Multivariate Longitudinal Observations: The Contaminated Gaussian Hidden Markov Model

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Discrete Beta Kernel Graduation of Age-Specific Demographic Indicators

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