No abstract

No abstract

This paper introduces a new family of exponential ratio estimators of population variance in stratified random sampling and studies its properties. Based on Bahl & Tuteja [1], Kadillar & Cingi [2] and Solanki et al. [3], membership of the new family of estimators is identified. Analytical and numerical results show that under certain prescribed conditions, the new estimator has equal optimal efficiency with the regression estimator of population variance but always fares better than the classical ratio estimator of population variance by Isaki [4] and every identified existing estimator of its family.

No abstract

The most challenging limitation of the ratio estimation is that of deriving variance estimator that admits more than two auxiliary variables. This paper introduces a new calibration weights that prompt the formulation of a multivariate ratio estimator by the calibration tuning parameter subject to a pooled-calibration constraint. Analytical framework for deriving variance estimator that admits as many auxiliary variables as desired is developed. The efficiency gains of the proposed estimator vis-a-vis the Generalized Regression (GREG) Estimator are studied through simulation. Simulation results proved the dominance of the new proposals over existing ones.