Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method

ABSTRACT

Mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets. In general, the error terms are assumed to be normal in the mixture regression model. However, the estimators under normality assumption are sensitive to the outliers. In this article, we introduce a robust mixture regression procedure based on the LTS-estimation method to combat with the outliers in the data. We give a simulation study and a real data example to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.

KEYWORDS:

• EM algorithm
• LTS-estimation method
• Mixture regression model
• Robust regression

MATHEMATICS SUBJECT CLASSIFICATION:

• 65C60
• 68U20
• 62F35

Acknowledgments

The authors thank the anonymous referees and the associate editor, whose comments and suggestions have greatly improved the article.