Model Selection for Gaussian Mixture Models

摘要：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.