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Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions

Author

Listed:
  • Alexa A. Sochaniwsky

    (McMaster University)

  • Michael P. B. Gallaugher

    (Baylor University)

  • Yang Tang

    (McMaster University)

  • Paul D. McNicholas

    (McMaster University)

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.

Suggested Citation

  • Alexa A. Sochaniwsky & Michael P. B. Gallaugher & Yang Tang & Paul D. McNicholas, 2025. "Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions," Journal of Classification, Springer;The Classification Society, vol. 42(1), pages 113-133, March.
    • Handle: RePEc:spr:jclass:v:42:y:2025:i:1:d:10.1007_s00357-024-09479-x
    DOI: 10.1007/s00357-024-09479-x
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    References listed on IDEAS

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    4. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
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