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Naik, P.A., Shi, P. and Tsai, C.-L. (2007) Extending the Akaike Information Criterionto Mixture Regression Models. Journal of the American Statistical Association, 102.

has been cited by the following article:

• 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.