Log Type Calibration Estimator of Population Mean in Stratified Sampling

In this paper, a log type calibration estimator for estimating the population mean in stratified random sampling has been proposed utilizing the available auxiliary information. This estimator has also been extended in case of double stratified sampling when mean of the auxiliary information is not known. The simulation study has been carried out using R software on real as well as artificial datasets. The empirical as well as bootstrap estimates of the percentage relative root mean squared error (%RRMSE) have been computed for real as well as artificial datasets. These suggested calibration estimators have been compared with the estimators given by Singh (Golden jubilee year 2003 of the linear regression estimator, 2003a) and Tracy et al. (Surv Methodol 29(1): 99–104, 2003).