Calibration estimator of population mean in stratified extreme ranked set sampling with simulation study

Abstract

Calibration estimation sets the original weights to include the known population characteristics of auxiliary variables using constraints. In this article, we have proposed a new calibration estimator of the population mean in stratified extreme ranked set sampling design, which is more efficient and cost-effective design against other sampling designs in the literature. A detailed simulation study is carried out to observe the performance of proposed estimators. We have used the information of auxiliary variable to avoid ranking errors in our simulations. We have created samples from a bi-variate normal distribution with different values of rho xy. While one of these variables is taken as the variable of interest, the other is accepted as an auxiliary variable and used in ranking the sample units in each set. As a result of the simulation study using both synthetic and real data sets, we have found that our proposed estimators are more efficient than Sinha et al. [19] calibration estimator and classical stratified estimator.