An improved calibrated estimator of population mean in stratified random sampling

Calibration technique is becoming more important in sample survey. Calibration estimation helps in improving the estimates of population parameters by making use of auxiliary information. This paper proposed a new calibrated estimator for estimating the population mean in stratified random sampling with a set of new calibration constrains using known coefficient of variation of the auxiliary variable. A new improved calibration weights are derived by using constraints with coefficient of variation of the auxiliary variable in addition to the constraint utilize by Tracy et al. (2003). An empirical comparison of the suggested estimator is carried out by using an artificial population to compare the result of the proposed method.