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Creators/Authors contains: "Yao, Weixin"

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  1. ; ; ( , International Statistical Review)
    Summary

    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.

     
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  2. ; ; ; ; ; ( , Biometrics)
    Full Text Available 
  3. ; ; ( , Energy Procedia)
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  4. ; ( , Scandinavian Journal of Statistics)
  5. ; ; ; ; ; ; ( , Hydrological Processes)