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Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering

Author

Listed:
  • Derek S. Young

    (University of Kentucky)

  • Xi Chen

    (University of Kentucky
    University of Kentucky)

  • Dilrukshi C. Hewage

    (University of Kentucky)

  • Ricardo Nilo-Poyanco

    (Universidad Mayor)

Abstract

Finite mixtures of (multivariate) Gaussian distributions have broad utility, including their usage for model-based clustering. There is increasing recognition of mixtures of asymmetric distributions as powerful alternatives to traditional mixtures of Gaussian and mixtures of t distributions. The present work contributes to that assertion by addressing some facets of estimation and inference for mixtures-of-gamma distributions, including in the context of model-based clustering. Maximum likelihood estimation of mixtures of gammas is performed using an expectation–conditional–maximization (ECM) algorithm. The Wilson–Hilferty normal approximation is employed as part of an effective starting value strategy for the ECM algorithm, as well as provides insight into an effective model-based clustering strategy. Inference regarding the appropriateness of a common-shape mixture-of-gammas distribution is motivated by theory from research on infant habituation. We provide extensive simulation results that demonstrate the strong performance of our routines as well as analyze two real data examples: an infant habituation dataset and a whole genome duplication dataset.

Suggested Citation

  • Derek S. Young & Xi Chen & Dilrukshi C. Hewage & Ricardo Nilo-Poyanco, 2019. "Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering," 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. 13(4), pages 1053-1082, December.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:4:d:10.1007_s11634-019-00361-y
    DOI: 10.1007/s11634-019-00361-y
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    1. Delong, Łukasz & Lindholm, Mathias & Wüthrich, Mario V., 2021. "Gamma Mixture Density Networks and their application to modelling insurance claim amounts," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 240-261.
    2. Mingxing He & Jiahua Chen, 2022. "Consistency of the MLE under a two-parameter Gamma mixture model with a structural shape parameter," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(8), pages 951-975, November.

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