New Calibration of Finite Population Mean of Combined Ratio Estimators in Stratified Random Sampling

Abstract

This study deals with using calibration estimation approaches to modified the combined ratio estimator in stratified random sampling. Calibration distance measures with their associate constraints were used to modify combined ratio estimator. In stratified random sampling, new sets of optimum calibration weights are created and used to obtain new calibration estimators of population mean. Empirical study through simulation was conducted to look into the efficiency of the suggested estimators obtained. The suggested calibration estimators are more efficient than other existing estimators investigated in the study, according to the findings.