TITLE:
Selecting the Quantity of Models in Mixture Regression
AUTHORS:
Dawei Lang, Wanzhou Ye
KEYWORDS:
Mixture Regression, Model Based Clustering, Information Criterion, AIC, BIC
JOURNAL NAME:
Advances in Pure Mathematics,
Vol.6 No.8,
July
25,
2016
ABSTRACT: 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.