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Statistical Models for Data Analysis pp 233–240Cite as

On Clustering and Classification Via Mixtures of Multivariate t-Distributions

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Abstract

The use of mixture models for clustering and classification has received renewed attention within the literature since the mid-1990s. The multivariate Gaussian distribution has been at the heart of this body of work, but approaches that utilize the multivariate t-distribution have burgeoned into viable and effective alternatives. In this paper, recent work on classification and clustering using mixtures of multivariate t-distributions is reviewed and discussed, along with related issues. The paper concludes with a summary and suggestions for future work.

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Authors and Affiliations

  1. University of Guelph, Ontario, Canada

    Paul D. McNicholas

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  1. Paul D. McNicholas
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Correspondence to Paul D. McNicholas .

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Editors and Affiliations

  1. Department of Economics, and Management, University of Pavia, Via San Felice 7, Pavia, 27100, Italy

    Paolo Giudici

  2. Department of Economics, and Business, University of Catania, Corso Italia 55, Catania, 95129, Italy

    Salvatore Ingrassia

  3. , Department of Statistics, University of Rome "La Sapienza", Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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McNicholas, P.D. (2013). On Clustering and Classification Via Mixtures of Multivariate t-Distributions. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_27

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