Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions
Ryan P. Browne, Luca Bagnato, Antonio Punzo · Advances in Data Analysis and Classification · 2023
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. Supplementary Information: The online version contains supplementary material available at 10.1007/s11634-023-00558-2.