Robust estimation of the number of components for mixtures of linear regression - Meng, Li (Kansas State University, 2014)
In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion is robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. A real data application is also used to illustrate the effectiveness of the trimmed model selection methods.
Keywords: Mixture of linear regression models; Model selection; Robustness; Trimmed likelihood estimator; Statistics (0463)