Cluster-Weighted Modeling is a flexible statistical framework for modeling local relationships in heterogeneous populations on the basis on weighted combinations of local models. Besides the traditional approach based on Gaussian assumptions, here we consider Cluster Weighted Modeling based on Student-t distributions. In this paper we present an EM algorithm for parameter estimation in Cluster-Weighted models according to the maximum likelihood approach.
Ingrassia, S., Minotti, S., Incarbone, G. (2010). The EM algorithm for Cluster-Weighted Modeling. Intervento presentato a: GFKL 2010 (34th Annual Conference of the German Classification Society), Karlsruhe.
The EM algorithm for Cluster-Weighted Modeling
MINOTTI, SIMONA CATERINA;
2010
Abstract
Cluster-Weighted Modeling is a flexible statistical framework for modeling local relationships in heterogeneous populations on the basis on weighted combinations of local models. Besides the traditional approach based on Gaussian assumptions, here we consider Cluster Weighted Modeling based on Student-t distributions. In this paper we present an EM algorithm for parameter estimation in Cluster-Weighted models according to the maximum likelihood approach.
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