COMPARISON OF SOME ROBUST REGRESSION METHODS IN CASE OF OUTLIER

Abstract

The presence of outliers or observations in the data set in the studies can significantly affect the statistical analysis results and modeling. The least squares method which is sensitive to outliers can also give misleading results when the assumptions are not met. In this case robust regression methods which are presented as an alternative to multiple linear regression, are used. In this study a sample data set was taken and a study was conducted to investigate how much the regression estimators could explain the data set in case of outliers within the observation points. For this purpose, in case of outliers, quantile regression method from robust regression methods, smallest absolute deviations (LAD), which is a special case of quantile regression method, commonly used M estimators among robust estimators, with S and MM estimators with high breakpoints were used.