Estimation of Location Parameters Under Nonnormal Errors and Quadratic Loss

Abstract

Assuming a statistical model in which the joint distribution of the unobservable errors is drawn from independent univariate Student t's that are identically and symmetrically distributed, the sampling performance of traditional robust estimators and a family of Stein-like estimators are compared and evaluated. These results suggest that under thick-tailed distributions, the relative sampling performances and risk characteristics for a range of nonconventional Stein estimators remains approximately the same as in the case of their normal counterparts. The empirical risk implications of misspecifying the error distribution are investigated.