Estimation and order selection for multivariate exponential power mixture models

Xiao Chen, Zhenghui Feng*, Heng Peng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Finite mixture model is a promising statistical model in investigating the heterogeneity of population. For multivariate non-Gaussian density estimation and approximation, in this paper, we consider to use multivariate exponential power mixture models. We propose the penalized-likelihood method with a generalized EM algorithm to estimate locations, scale matrices, shape parameters, and mixing probabilities. Order selection is achieved simultaneously. Properties of the estimated order have been derived. Although we mainly focus on the unconstrained scale matrix type in multivariate exponential power mixture models, three more parsimonious types of scale matrix have also been considered. Performance based on simulation and real data analysis implies the parsimony of the exponential power mixture models, and verifies the consistency of order selection.

Original languageEnglish
Article number105140
Number of pages23
JournalJournal of Multivariate Analysis
Volume195
Early online date8 Dec 2022
DOIs
Publication statusPublished - May 2023

Scopus Subject Areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • Exponential power family
  • Finite mixture models
  • Order selection

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