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A Selective Overview and Comparison of Robust Mixture Regression Estimators

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  • Chun Yu
  • Weixin Yao
  • Guangren Yang

Abstract

Mixture regression models have been widely used in business, marketing and social sciences to model mixed regression relationships arising from a clustered and thus heterogeneous population. The unknown mixture regression parameters are usually estimated by maximum likelihood estimators using the expectation–maximisation algorithm based on the normality assumption of component error density. However, it is well known that the normality‐based maximum likelihood estimation is very sensitive to outliers or heavy‐tailed error distributions. This paper aims to give a selective overview of the recently proposed robust mixture regression methods and compare their performance using simulation studies.

Suggested Citation

  • Chun Yu & Weixin Yao & Guangren Yang, 2020. "A Selective Overview and Comparison of Robust Mixture Regression Estimators," International Statistical Review, International Statistical Institute, vol. 88(1), pages 176-202, April.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:1:p:176-202
    DOI: 10.1111/insr.12349
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    References listed on IDEAS

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