Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Alexa A. Sochaniwsky, Michael P. B. Gallaugher (), Yang Tang and Paul D. McNicholas
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Alexa A. Sochaniwsky: McMaster University
Michael P. B. Gallaugher: Baylor University
Yang Tang: McMaster University
Paul D. McNicholas: McMaster University

Journal of Classification, 2025, vol. 42, issue 1, No 7, 113-133

Abstract: Abstract Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric. Traditional approaches to model-based clustering often fail for high dimensional data, e.g., due to the number of free covariance parameters. A parametrization of the component scale matrices for the mixture of generalized hyperbolic distributions is proposed. This parameterization includes a penalty term in the likelihood. An analytically feasible expectation-maximization algorithm is developed by placing a gamma-lasso penalty constraining the concentration matrix. The proposed methodology is investigated through simulation studies and illustrated using two real datasets.

Keywords: Asymmetric clusters; Flexible clustering; Generalized hyperbolic distributions; GHD-GLS; Penalized likelihood; Sparse mixture models (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00357-024-09479-x

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