Robust estimators and properties

Robust estimators are used for reducing the effects(weights) of outlying observations in the data set to get more reliable and stable estimators. The aim of this thesis is to propose robust regression procedures as an alternative method to Least Squares procedure which is widely used in classical regression analysis and very sensitive to outlying observations. In this thesis, firstly outlier and breaking point concepts will be introduced, secondly a general overview of estimators for robust simple and multiple regression will be given and finally these estimators will be compared with classical Least Squares estimators and examples will be provided. Keywords: Breaking Point, , Least median squares estimator, Least squares estimator, Outlier, Robust estimator,