Cluster-Weighted Modeling is a flexible statistical framework for modeling local relationships in heterogeneous populations on the basis of 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. © 2012 Springer-Verlag Berlin Heidelberg.
Ingrassia, S., Minotti, S., Incarbone, G. (2012). An EM algorithm for the Student-t Cluster-Weighted Modeling. In W. Gaul, A. Geyer-Schulz, L. Schmidt-Thieme, J. Kunze (a cura di), Challenges at the Interface of Data Analysis, Computer Science, and Optimization (pp. 13-21). Kluwer Academic Publishers [10.1007/978-3-642-24466-7-2].
An EM algorithm for the Student-t Cluster-Weighted Modeling
MINOTTI, SIMONA CATERINA;
2012
Abstract
Cluster-Weighted Modeling is a flexible statistical framework for modeling local relationships in heterogeneous populations on the basis of 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. © 2012 Springer-Verlag Berlin Heidelberg.
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