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Challenges at the Interface of Data Analysis, Computer Science, and Optimization pp 13–21Cite as

An EM Algorithm for the Student-t Cluster-Weighted Modeling

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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.

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References

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Author information

Authors and Affiliations

  1. Dipartimento di Impresa, Culture e Società, Università di Catania, Corso Italia 55, 95129, Catania, Italy

    Salvatore Ingrassia & Giuseppe Incarbone

  2. Dipartimento di Statistica, Università di Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126, Milano, Italy

    Simona C. Minotti

Authors
  1. Salvatore Ingrassia
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  2. Simona C. Minotti
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  3. Giuseppe Incarbone
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Corresponding author

Correspondence to Simona C. Minotti .

Editor information

Editors and Affiliations

  1. Fak. Wirtschaftswissenschaften, Inst. Entscheidungstheorieund, Universität Karlsruhe (TH), Kaiserstr. 12, Karlsruhe, 76128, Germany

    Wolfgang A. Gaul

  2. Insitute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, Karlsruhe, 76131, Baden-Württemberg, Germany

    Andreas Geyer-Schulz

  3. , Information Systems, University ofHildesheim, Marienburger Platz 22, Hildesheim, 31141, Germany

    Lars Schmidt-Thieme

  4. Institute for Information Systems, and Management (IISM), Karlsruhe Institute of Technology (KIT), Kaiserstraße 12, Karlsruhe, 76128, Germany

    Jonas Kunze

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© 2012 Springer-Verlag Berlin Heidelberg

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Ingrassia, S., Minotti, S.C., Incarbone, G. (2012). An EM Algorithm for the Student-t Cluster-Weighted Modeling. In: Gaul, W., Geyer-Schulz, A., Schmidt-Thieme, L., Kunze, J. (eds) Challenges at the Interface of Data Analysis, Computer Science, and Optimization. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24466-7_2

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