报告题目：Model Selection for Gaussian Mixture Models
报告人：Heng Peng 香港浸会大学数学系副教授
报告时间：12月2日周五下午15：00-16：00
报告地点：统计楼103
报告摘要：
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.