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arXiv · 1302.2373

Parsimonious Skew Mixture Models for Model-Based Clustering and Classification

Irene Vrbik·Paul D. McNicholas
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Abstract

In recent work, robust mixture modelling approaches using skewed distributions have been explored to accommodate asymmetric data. We introduce parsimony by developing skew-t and skew-normal analogues of the popular GPCM family that employ an eigenvalue decomposition of a positive-semidefinite matrix. The methods developed in this paper are compared to existing models in both an unsupervised and semi-supervised classification framework. Parameter estimation is carried out using the expectation-maximization algorithm and models are selected using the Bayesian information criterion. The efficacy of these extensions is illustrated on simulated and benchmark clustering data sets.

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BibTeXRIS

Irene Vrbik, Paul D. McNicholas. 2013-02-10. Parsimonious Skew Mixture Models for Model-Based Clustering and Classification. https://doi.org/10.1016/j.csda.2013.07.008

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