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A mixture model for skewed mixed-type data

Author

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  • Alamer, Eman M.S.
  • Gallaugher, Michael P.B.
  • McNicholas, Paul D.

Abstract

Many approaches exist for clustering categorical or continuous data. However, there are few options for mixed-type data, especially when the clusters exhibit skewness and/or heavy tails in the continuous variables. A model-based clustering approach is proposed to help address this gap.

Suggested Citation

  • Alamer, Eman M.S. & Gallaugher, Michael P.B. & McNicholas, Paul D., 2025. "A mixture model for skewed mixed-type data," Statistics & Probability Letters, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:stapro:v:226:y:2025:i:c:s016771522500152x
    DOI: 10.1016/j.spl.2025.110507
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    References listed on IDEAS

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    1. Damien McParland & Isobel Claire Gormley, 2016. "Model based clustering for mixed data: clustMD," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(2), pages 155-169, June.
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