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Huang, Tao; Peng, Heng; Zhang, Kun
Statistica Sinica, 01/2017, Volume: 27, Issue: 1Journal Article
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 multi variate 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.
