Model-based clustering using a new multivariate skew distribution

Salvatore D. Tomarchio (), Luca Bagnato and Antonio Punzo
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Salvatore D. Tomarchio: University of Catania
Luca Bagnato: Catholic University of the Sacred Heart
Antonio Punzo: University of Catania

Advances in Data Analysis and Classification, 2024, vol. 18, issue 1, No 4, 83 pages

Abstract: 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.

Keywords: Mixture models; Skewed data; Model-based clustering; Cryptocurrencies; 62H10; 62H30 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11634-023-00552-8

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