Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions

Ryan P. Browne (), Luca Bagnato () and Antonio Punzo ()
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Ryan P. Browne: University of Waterloo
Luca Bagnato: Catholic University of the Sacred Heart
Antonio Punzo: University of Catania

Advances in Data Analysis and Classification, 2024, vol. 18, issue 3, No 4, 597-625

Abstract: Abstract Mixtures of multivariate leptokurtic-normal distributions have been recently introduced in the clustering literature based on mixtures of elliptical heavy-tailed distributions. They have the advantage of having parameters directly related to the moments of practical interest. We derive two estimation procedures for these mixtures. The first one is based on the majorization-minimization algorithm, while the second is based on a fixed point approximation. Moreover, we introduce parsimonious forms of the considered mixtures and we use the illustrated estimation procedures to fit them. We use simulated and real data sets to investigate various aspects of the proposed models and algorithms.

Keywords: Leptokurtic-normal distribution; Majorization–minimization algorithm; Mixture models; Parsimony; 62Hxx Multivariate analysis, 62H30 Classification and discrimination; cluster analysis (statistical aspects); mixture models, 62H12 Estimation in multivariate analysis, 62G07 Density estimation (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11634-023-00558-2

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