Calibration Weighting for Estimation of Finite Population Mean in Stratified Adaptive Cluster Sampling
Abstract
This paper introduces calibrated estimators for population mean in case of rare and hidden populations using stratified adaptive cluster sampling scheme. Three cases of the suggested calibration estimator have been considered applying mean, logarithmic mean, and exponential mean of the available auxiliary variable in the calibration constraints. The developed estimators have been compared with the existing estimators given by Thompson (1991), Kadilar and Cingi (2005) and Chutiman (2010) as well as traditional combined ratio estimator in case of stratified random sampling and stratified adaptive cluster sampling. The simulation study has also been conducted using R-software. The suggested calibration estimators for stratified adaptive cluster sampling are found to be more efficient than the other existing estimators under similar setup.
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