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

Abstract

Calibration estimation set 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 (SERSS), which is a 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. This paper, we used the auxiliary variable approach to avoid ranking errors in our simulations. In this case, we first created samples from a bivariate normal distribution with different values of qxy . One of these variables is taken as the variable of interest, while the remaining one was considered an auxiliary variable and used to ranking of the sample units within each set. As a result of the simulation study using both synthetic data and real data set, we have found that our proposed estimators are more efficient than Sinha et al. (2017) calibration estimator and classical stratified estimator.