A robust method of estimation based on the MML estimators for a single linear regression model

Abstract

We adopt the modified maximum likelihood approach (based on symmetrically Type-II censored normal samples) of Tiku (1967, 1970) and obtain a robust method of estimation of the parameters in a simple linear regression model. By simulating the bias and mean square error of these estimators under both normal and mixture-normal models for the error component and then comparing them with the corresponding values of the ordinary least-square estimators (or the MLE's under normality), we display their efficiency and robustness features. We also obtain the asymptotic variances and covariances under normality of these estimators via the information matrix. Finally, we consider a numerical example and illustrate the method of estimation developed in this paper.