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

Ryan P. Browne, Luca Bagnato*, Antonio Punzo

*Corresponding author

Research output: Contribution to journalArticle


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.
Original languageEnglish
Pages (from-to)1-29
Number of pages29
JournalAdvances in Data Analysis and Classification
Publication statusPublished - 2023


  • Majorization–minimization algorithm
  • Parsimony
  • Leptokurtic-normal distribution


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