Mixtures of skewed matrix variate bilinear factor analyzers

Michael P. B. Gallaugher and Paul D. McNicholas ()
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Michael P. B. Gallaugher: McMaster University
Paul D. McNicholas: McMaster University

Advances in Data Analysis and Classification, 2020, vol. 14, issue 2, No 8, 415-434

Abstract: Abstract In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such techniques are firmly established in the literature for multivariate data, there is a relative paucity in the area of matrix variate, or three-way, data. Furthermore, the few methods that are available all assume matrix variate normality, which is not always sensible if cluster skewness or excess kurtosis is present. Mixtures of bilinear factor analyzers using skewed matrix variate distributions are proposed. In all, four such mixture models are presented, based on matrix variate skew-t, generalized hyperbolic, variance-gamma, and normal inverse Gaussian distributions, respectively.

Keywords: Clustering; Factor analysis; Kurtosis; Skewed; Matrix variate distribution; Mixture models; 62H30; 62H25 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11634-019-00377-4

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