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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CITATION STYLE
Ingrassia, S., Minotti, S. C., & Incarbone, G. (2012). An em algorithm for the student-t cluster-weighted modeling. In Studies in Classification, Data Analysis, and Knowledge Organization (pp. 13–21). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-642-24466-7_2
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