Journal of Classification

, Volume 29, Issue 3, pp 363–401

Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions


    • Dipartimento di Economia e ImpresaUniversità di Catania
  • Simona C. Minotti
    • Dipartimento di StatisticaUniversità di Milano-Bicocca
  • Giorgio Vittadini
    • Dipartimento di Metodi Quantitativi per l’Economia e le Scienze AziendaliUniversità di Milano-Bicocca

DOI: 10.1007/s00357-012-9114-3

Cite this article as:
Ingrassia, S., Minotti, S.C. & Vittadini, G. J Classif (2012) 29: 363. doi:10.1007/s00357-012-9114-3


Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. Under Gaussian assumptions, we investigate statistical properties of CWM from both theoretical and numerical point of view; in particular, we show that Gaussian CWM includes mixtures of distributions and mixtures of regressions as special cases. Further, we introduce CWM based on Student-t distributions, which provides a more robust fit for groups of observations with longer than normal tails or noise data. Theoretical results are illustrated using some empirical studies, considering both simulated and real data. Some generalizations of such models are also outlined.


Cluster-weighted modelingMixture modelsModel-based clustering

Copyright information

© Springer Science+Business Media, LLC 2012