A comparative study of some robust methods for coefficient-estimation in linear regression

Robust regression estimators are known to perform well in the presence of outliers. Although theoretical properties of these estimators have been derived, there is always a need for empirical results to assist their implementation in practical situations. A simulation study of four robust alternatives to the least-squares method is presented within a set of error-distributions which includes many outlier-generating models. The robustness and efficiency features of the methods are exhibited, some finite-sample results are discussed in combination with asymptotic properties, and the relative merits of the estimators are viewed in connection with the tail-length of the underlying error-distribution.