Two new matrix-variate distributions with application in model-based clustering

Luca Bagnato, Salvatore D. Tomarchio, Antonio Punzo

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Two matrix-variate distributions, both elliptical heavy-tailed generalization of the matrix-variate normal distribution, are introduced. They belong to the normal scale mixture family, and are respectively obtained by choosing a convenient shifted exponential or uniform as mixing distribution. Moreover, they have a closed-form for the probability density function that is characterized by only one additional parameter, with respect to the nested matrix-variate normal, governing the tail-weight. Both distributions are then used for model-based clustering via finite mixture models. The resulting mixtures, being able to handle data with atypical observations in a better way than the matrix-variate normal mixture, can avoid the disruption of the true underlying group structure. Different EM-based algorithms are implemented for parameter estimation and tested in terms of computational times and parameter recovery. Furthermore, these mixture models are fitted to simulated and real datasets, and their fitting and clustering performances are analyzed and compared to those obtained by other well-established competitors.
Original languageEnglish
Pages (from-to)1-22
Number of pages22
JournalCOMPUTATIONAL STATISTICS & DATA ANALYSIS
DOIs
Publication statusPublished - 2020

Keywords

  • Clustering
  • Heavy-tailed distributions
  • Matrix-variate
  • Mixture models

Fingerprint

Dive into the research topics of 'Two new matrix-variate distributions with application in model-based clustering'. Together they form a unique fingerprint.

Cite this