Research-Model Selection for Gaussian Mixture Models

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Model Selection for Gaussian Mixture Models

Speaker: Peng Heng
Time: Feb 24, 2016, 15:00-16:00
Location: Conference Room 703, Service Center of Scientific Research and Teaching

This talk is concerned with an important issue in finite mixture modeling, namely 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 statistically 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 real data analysis are presented to illustrate the performance of the proposed method.