Robust mixture regression estimation based on least : trimmed median method by using several models

Abstract EN

In this paper we provide one of the robust mixture regression estimators, least trimmed median (LTM) method.

It is known that mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets.

In general, the error terms are assumed to be normal in the mixture regression model.

However, the estimators under normality assumption are sensitive to outliers.

Therefore, we introduced a robust mixture regression procedure based on the LTM-estimation method to combat with the outliers in the data.

In this paper we handle LTM method by using three mixture regression models; Laplace, t and normal distributions.

A simulation study is applied to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.