Mixtures of skew-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si111.gif" display="inline" overflow="scroll"><mml:mi>t</mml:mi></mml:math>factor analyzers
Computational Statistics & Data AnalysisPublished 29 March 2014Open access
Paula Murray, Ryan P. Browne, Paul D. McNicholas
Citations100
SJR quartileQ1
SJR score0.89
SNIP1.38
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TL;DR
Mixtures of skew-t factor analyzers are very well-suited for model-based clustering of high-dimensional data, giving superior clustering results when compared to a well-established family of Gaussian mixture models.
Abstract
A mixture of skew-t factor analyzers is introduced as well as a family of mixture models based thereon. The particular formulation of the skew-t distribution used arises as a special case of the generalized hyperbolic distribution. Like their Gaussian and t-distribution analogues, mixtures of skew-t factor analyzers are very well-suited for model-based clustering of high-dimensional data. The alternating expectation–conditional maximization algorithm is used for model parameter estimation and the Bayesian information criterion is used for model selection. The models are applied to both real and simulated data, giving superior clustering results when compared to a well-established family of Gaussian mixture models.
Keywords
Computer ScienceMathematicsDecision Sciences
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