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A Selective Overview and Comparison of Robust Mixture Regression Estimators
Author(s) -
Yu Chun,
Yao Weixin,
Yang Guangren
Publication year - 2020
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/insr.12349
Subject(s) - outlier , statistics , estimator , regression analysis , mathematics , robust regression , normality , econometrics , regression , mixture model , expectation–maximization algorithm , robust statistics , maximum likelihood
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.