Improving the Efficiency of Ratio Estimators by Calibration Weightings
*Etebong P. Clement1 and Elisha J. Inyang2
1,2Department of Statistics, University of Uyo, P. M. B. 1017, Uyo - Nigeria
*Corresponding Author Email: email@example.com; firstname.lastname@example.org
It is observed that the performances of most improved ratio estimators depend on some optimality conditions that need to be satisfied to guarantee better estimator. This paper develops a new approach to ratio estimation that produces a more efficient class of ratio estimators that do not depend on any optimality conditions for optimum performance using calibration weightings. The relative performances of the proposed calibration ratio estimators are compared with a corresponding global [Generalized Regression (GREG)] estimator. Results of analysis showed that the proposed calibration ratio estimators are substantially superior to the traditional GREG-estimator with relatively small bias, mean square error, average length of confidence interval and coverage probability. In general, the proposed calibration ratio estimators are more efficient than all existing estimators considered in the study.
Keywords: efficiency comparison, existing estimators, global estimator, optimality, conditions, ratio estimator