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Your search keyword '"Punzo, Antonio"' showing total 2 results
Start Over Author "Punzo, Antonio" Topic cluster analysis (statistics) Topic gaussian mixture models
2 results on '"Punzo, Antonio"'

1. Erratum to: The Generalized Linear Mixed Cluster-Weighted Model.

Author

Ingrassia, Salvatore, Punzo, Antonio, Vittadini, Giorgio, and Minotti, Simona

Subjects
*CLUSTER analysis (Statistics), *ALGORITHMS, *GAUSSIAN mixture models
Abstract

Cluster-weighted models (CWMs) are a flexible family of mixture models for fitting the joint distribution of a random vector composed of a response variable and a set of covariates. CWMs act as a convex combination of the products of the marginal distribution of the covariates and the conditional distribution of the response given the covariates. In this paper, we introduce a broad family of CWMs in which the component conditional distributions are assumed to belong to the exponential family and the covariates are allowed to be of mixed-type. Under the assumption of Gaussian covariates, sufficient conditions for model identifiability are provided. Moreover, maximum likelihood parameter estimates are derived using the EM algorithm. Parameter recovery, classification assessment, and performance of some information criteria are investigated through a broad simulation design. An application to real data is finally presented, with the proposed model outperforming other well-established mixture-based approaches. [ABSTRACT FROM AUTHOR]

Published
2015
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2. Model-based clustering via linear cluster-weighted models.

Author

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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