Parsimonious Skew Mixture Models for Model-Based Clustering and Classification

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.

References in corpus (3)

• Natural Scales in Geographical Patterns
• Estimating Common Principal Components in High Dimensions
• Parsimonious Shifted Asymmetric Laplace Mixtures

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