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Model-based clustering using a new multivariate skew distribution

Author

Listed:
  • Salvatore D. Tomarchio

    (University of Catania)

  • Luca Bagnato

    (Catholic University of the Sacred Heart)

  • Antonio Punzo

    (University of Catania)

Abstract

Quite often real data exhibit non-normal features, such as asymmetry and heavy tails, and present a latent group structure. In this paper, we first propose the multivariate skew shifted exponential normal distribution that can account for these non-normal characteristics. Then, we use this distribution in a finite mixture modeling framework. An EM algorithm is illustrated for maximum-likelihood parameter estimation. We provide a simulation study that compares the fitting performance of our model with those of several alternative models. The comparison is also conducted on a real dataset concerning the log returns of four cryptocurrencies.

Suggested Citation

  • Salvatore D. Tomarchio & Luca Bagnato & Antonio Punzo, 2024. "Model-based clustering using a new multivariate skew distribution," 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. 18(1), pages 61-83, March.
    • Handle: RePEc:spr:advdac:v:18:y:2024:i:1:d:10.1007_s11634-023-00552-8
    DOI: 10.1007/s11634-023-00552-8
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