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Open Access Publications from the University of California

Robust estimation of the number of components for mixtures of linear regression models

  • Author(s): Li, M
  • Xiang, S
  • Yao, W
  • et al.

© 2015, Springer-Verlag Berlin Heidelberg. In this paper, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criteria. Compared to the traditional information criteria, the trimmed criteria are 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. Two real data applications are also used to illustrate the effectiveness of the trimmed model selection methods.

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