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15-02-2023

Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions

Authors: Utkarsh J. Dang, Michael P.B. Gallaugher, Ryan P. Browne, Paul D. McNicholas

Published in: Journal of Classification | Issue 1/2023

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Abstract

Families of mixtures of multivariate power exponential (MPE) distributions have already been introduced and shown to be competitive for cluster analysis in comparison to other mixtures of elliptical distributions, including mixtures of Gaussian distributions. A family of mixtures of multivariate skewed power exponential distributions is proposed that combines the flexibility of the MPE distribution with the ability to model skewness. These mixtures are more robust to variations from normality and can account for skewness, varying tail weight, and peakedness of data. A generalized expectation-maximization approach, which combines minorization-maximization and optimization based on accelerated line search algorithms on the Stiefel manifold, is used for parameter estimation. These mixtures are implemented both in the unsupervised and semi-supervised classification frameworks. Both simulated and real data are used for illustration and comparison to other mixture families.

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Appendix
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Metadata
Title
Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions
Authors
Utkarsh J. Dang
Michael P.B. Gallaugher
Ryan P. Browne
Paul D. McNicholas
Publication date
15-02-2023
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2023
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-022-09427-7

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