The robustness and efficiency of trimmed elemental estimation in regression analysis: a Monte Carlo simulation study

Mayo and Gray [Am Statist 51 (1997) 122] introduced the leverage-residual weighted elemental (LRWE) classification of regression estimators and proposed a new method of estimation called trimmed elemental estimation (TEE). In this article, we perform a simulation study of the efficiency of certain TEE estimators relative to ordinary least squares under normal errors and their robustness under various non-normal error distributions in the context of the simple linear regression model. Comparisons among these estimators are made on the basis of mean square error and percentiles of the absolute estimation errors in the simulations.