ELSEVIER Computational Statistics & Data Analysis 23 (1997) 525-540 COMPUTATIONAL STATISTICS & DATA ANALYSIS A comparative study of some robust methods for coefficient-estimation in linear regression S.G. Meintanis, G.S. Donatos* Departments of Economics, Universityof Athens, Athens, Greece Received August 1995; revised February 1996 Abstract 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. Keywords: Simulation; Outliers; Heavy-tailed distributions; Least median of Squares; Functional least squares; Trimmed least squares 1. Introduction An important issue in linear regression is the performance of coefficient-estima- tion procedures when the error term is not normally distributed. In such a case, the least-squares (LS) method has certain disadvantages. A major disadvantage is that the LS possesses a breakdown point (Donoho and Huber, 1983), which tends to zero as the sample size increases. Consequently, observations lying far away from the bulk of the data can cause the LS-estimator to be carried beyond all bounds. *Corresponding author. 0167-9473/97/$17.00 © 1997 Elsevier Science B.V. All rights reserved PII S0 1 67-9473(96)00046- 1