Model Selection for Gaussian Mixture Models
This addresses model selection in mixture modeling, which is crucial for statistical applications, but appears incremental as it builds on existing penalized likelihood approaches.
The paper tackles the problem of selecting the number of components in Gaussian mixture models by proposing a new penalized likelihood method, which is shown to be statistically consistent and demonstrated through simulations and real data analysis.
This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of the number of components. A modified EM algorithm is developed to simultaneously select the number of components and to estimate the mixing weights, i.e. the mixing probabilities, and unknown parameters of Gaussian distributions. Simulations and a real data analysis are presented to illustrate the performance of the proposed method.