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Journal of Classification
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12-07-2024 | Original Research

Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Authors: Alexa A. Sochaniwsky, Michael P. B. Gallaugher, Yang Tang, Paul D. McNicholas

Published in: Journal of Classification | Issue 1/2025

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Abstract

The article introduces a flexible clustering method using a sparse mixture of generalized hyperbolic distributions, addressing the limitations of traditional Gaussian mixture models in handling asymmetric and heavy-tailed data. The method is particularly effective in high-dimensional scenarios, where traditional methods may fail due to the number of free covariance parameters. By incorporating a sparse covariance matrix with a gamma-lasso penalty, the proposed approach enables the development of an analytically feasible EM algorithm. The article also presents simulation studies and real-data analyses to demonstrate the superior performance of the proposed method compared to existing approaches like parsimonious Gaussian mixture models and mixtures of generalized hyperbolic distributions. The GHD-GLS approach not only improves clustering accuracy but also provides a simpler interpretation of the clustering results by revealing different sparse correlation structures among variables.
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Metadata
Title
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
Authors
Alexa A. Sochaniwsky
Michael P. B. Gallaugher
Yang Tang
Paul D. McNicholas
Publication date
12-07-2024
Publisher
Springer US
Published in
Journal of Classification / Issue 1/2025
Print ISSN: 0176-4268
Electronic ISSN: 1432-1343
DOI
https://doi.org/10.1007/s00357-024-09479-x

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