Author(s)

Dawei Lang, Wanzhou Ye

College of Science, Shanghai University, Shanghai, China.

Mixture regression is a regression problem with mixed data. Specifically, in the observations, some data are from one model, while others from other models. Only after assuming the quantity of the model is given, EM or other algorithms can be used to solve this problem. We propose an information criterion for mixture regression model in this paper. Compared to ordinary information citizen by data simulations, results show our citizen has better performance on choosing the correct quantity of models.

Mixture Regression, Model Based Clustering, Information Criterion, AIC, BIC

Lang, D. and Ye, W. (2016) Selecting the Quantity of Models in Mixture Regression. Advances in Pure Mathematics, 6, 555-563. doi: 10.4236/apm.2016.68044.