Calibration Estimation in Stratified Random Sampling Using Three Constraints

Abstract

Calibration estimation improves the precision of population parameter estimates by incorporating specified auxiliary information. In this article, we have proposed a new calibration estimator for estimating the population mean in stratified sampling, based on three new calibration constraints derived from the coefficient of variation and correlation. A simulation study is conducted to evaluate the performance of the proposed estimator.