Antonio Punzo
University of Catania
Computational Statistics & Data Analysis | 2014
Salvatore Ingrassia; S Minotti; Antonio Punzo
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.
Advanced Data Analysis and Classification | 2013
Sanjeena Subedi; Antonio Punzo; Salvatore Ingrassia; Paul D. McNicholas
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
Journal of Classification | 2015
Salvatore Ingrassia; Antonio Punzo; Giorgio Vittadini; S Minotti
Statistical Methods and Applications | 2015
Sanjeena Subedi; Antonio Punzo; Salvatore Ingrassia; Paul D. McNicholas
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Statistical Methods and Applications | 2011
Francesca Greselin; Salvatore Ingrassia; Antonio Punzo
Journal of Time Series Analysis | 2012
Luca Bagnato; Antonio Punzo; Orietta Nicolis
and by a vector
Journal of Statistical Computation and Simulation | 2016
Antonio Punzo; Ryan P. Browne; Paul D. McNicholas
Journal of Computational and Graphical Statistics | 2016
Antonio Punzo; Antonello Maruotti
{\varvec{X}}
The American Statistician | 2013
Francesca Greselin; Antonio Punzo
Archive | 2011
Angelo Mazza; Antonio Punzo
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