Variance Estimation Using Auxiliary Information

Abstract

Variance estimators under several sample designs are proposed and compared when auxiliary information is available. Improvement of the bias and mean squared error over some estimators commonly used in practice is illustrated. A Monte Carlo comparison of the estimators is also presented.