Statistics and Computing

, Volume 10, Issue 4, pp 339–348

Robust mixture modelling using the t distribution

Authors

  • D. Peel
    • Department of MathematicsUniversity of Queensland
  • G. J. McLachlan
    • Department of MathematicsUniversity of Queensland
Article

DOI: 10.1023/A:1008981510081

Cite this article as:
Peel, D. & McLachlan, G.J. Statistics and Computing (2000) 10: 339. doi:10.1023/A:1008981510081
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Abstract

Normal mixture models are being increasingly used to model the distributions of a wide variety of random phenomena and to cluster sets of continuous multivariate data. However, for a set of data containing a group or groups of observations with longer than normal tails or atypical observations, the use of normal components may unduly affect the fit of the mixture model. In this paper, we consider a more robust approach by modelling the data by a mixture of t distributions. The use of the ECM algorithm to fit this t mixture model is described and examples of its use are given in the context of clustering multivariate data in the presence of atypical observations in the form of background noise.

finite mixture modelsnormal componentsmultivariate t componentsmaximum likelihoodEM algorithmcluster analysis
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© Kluwer Academic Publishers 2000