Model selection for Gaussian mixture models

Tao Huang, Heng PENG, Kun Zhang

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

This paper is concerned with an important issue in finite mixture modeling, the selection of the number of mixing components. A new penalized likelihood method is proposed for finite multivariate Gaussian mixture models, and it is shown to be consistent in determining the number of components. A modified EM algorithm is developed to simultaneously select the number of components and estimate the mixing probabilities and the unknown parameters of Gaussian distributions. Simulations and a data analysis are presented to illustrate the performance of the proposed method.

Original languageEnglish
Pages (from-to)147-169
Number of pages23
JournalStatistica Sinica
Volume27
Issue number1
DOIs
Publication statusPublished - Jan 2017

Scopus Subject Areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

User-Defined Keywords

  • EM algorithm
  • Gaussian mixture models
  • Model selection
  • Penalized likelihood

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