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.

中文翻译：

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鲁棒混合回归估计器的选择性概述和比较

混合回归模型已广泛用于商业，市场营销和社会科学中，以模拟由聚类且因此异类的人口产生的混合回归关系。未知的混合回归参数通常由最大似然估计器使用期望最大化算法基于分量误差密度的正态性假设进行估计。但是，众所周知，基于正态性的最大似然估计对异常值或尾部错误分布非常敏感。本文旨在对最近提出的鲁棒混合回归方法进行选择性概述，并通过仿真研究比较它们的性能。