Mean Squared Error Matrix comparison of Least Squares and Stein-Rule Estimators for Regression Coefficients under Non-normal Disturbances

Choosing the performance criterion to be mean squared error matrix,
we have compared the least squares and Stein-rule estimators for
coefficients in a linear regression model when the disturbances are
not necessarily normally distributed. It is shown that none of the
two estimators dominates the other, except in the trivial case of
merely one regression coefficient where least squares is found to
be superior in comparisons to Stein-rule estimators.