Trimmed Likelihood Estimation of Location and Scale of the Normal Distribution

Summary Consideration of multivariate statistical procedures leads to description of robust methods of estimation of location and scale for real random variables. This paper outlines asymptotic theory for estimating location and scale by viewing it as a trimmed likelihood estimator. An appeal is made to results from empirical processes linking the proofs to compact differentiability of estimating functionals. The estimator for location does not depend on the scale estimate and is robust against asymmetric contamination. Coincidentally the location estimator denned at the normal distribution is similar to the least trimmed squares estimator. Simulations corroborate the asymptotic theory and illustrate robustness against asymmetric contamination.