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

Author

Listed:
  • Meng Li

    (Kansas State University)

  • Sijia Xiang

    (Zhejiang University of Finance and Economics)

  • Weixin Yao

    (University of California)

Abstract

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.

Suggested Citation

  • Meng Li & Sijia Xiang & Weixin Yao, 2016. "Robust estimation of the number of components for mixtures of linear regression models," Computational Statistics, Springer, vol. 31(4), pages 1539-1555, December.
    • Handle: RePEc:spr:compst:v:31:y:2016:i:4:d:10.1007_s00180-015-0610-x
    DOI: 10.1007/s00180-015-0610-x
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    References listed on IDEAS

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