article · BOA (University of Milano-Bicocca) · 2010

The EM algorithm for Cluster-Weighted Modeling

Salvatore Ingrassia, Simona C. Minotti, Giuseppe Incarbone

Cited by 0Abstract and details only

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.

Cite this paper

APA 7

Ingrassia, S., Minotti, S. C., & Incarbone, G. (2010). The EM algorithm for Cluster-Weighted Modeling. BOA (University of Milano-Bicocca).

MLA 9

Ingrassia, Salvatore, et al. “The EM algorithm for Cluster-Weighted Modeling.” BOA (University of Milano-Bicocca), 2010.

Chicago

Ingrassia, Salvatore, Simona C. Minotti, and Giuseppe Incarbone. 2010. “The EM algorithm for Cluster-Weighted Modeling.” BOA (University of Milano-Bicocca).

Harvard

Ingrassia, S., Minotti, S.C. and Incarbone, G. (2010) ‘The EM algorithm for Cluster-Weighted Modeling’, BOA (University of Milano-Bicocca).

BibTeX

@article{ingrassia2010algorithm,
  title = {{The EM algorithm for Cluster-Weighted Modeling}},
  author = {Ingrassia, Salvatore and Minotti, Simona C. and Incarbone, Giuseppe},
  journal = {BOA (University of Milano-Bicocca)},
  year = {2010}
}

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