This paper considers the choice of scalars characterizing the double k-class estimators of the coefficients in a linear regression model. We demonstrate the existence of a double k-class estimator that dominates the least squares and Stein-rule estimators and we give feasible values for the...

In this note we furnish a set-up under which the Stein-rule estimator turns out to be a feasible version of the minimum risk estimator.

For estimating the coefficients in a linear regression model, the Stein-rule estimators are considered and their performance is studied according to the criterion of Pitman closeness. For this purpose, an asymptotic approximation for the criterion is derived and analyzed.

For the estimation of the mean [mu] of a normal population with unknown variance [sigma]2, Searles (1964) provides the minimum mean squared (MMSE) estimator (1 + [sigma]2/(n[mu]2))-1 in the class of all estimators of the type . This MMSE estimator however is not computable in practice if...