A generalized two-step calibrated ratio estimators of population mean in stratified random sampling

This paper develops a flexible and efficient family of two-step calibration ratio estimators for estimating the finite population means in stratified random sampling. The statistical properties of this family are derived up to first-order approximation using Taylor’s series approximation method. An existing calibration estimator has shown to be a member of the generalized two-step calibration ratio estimator for the case of a unit constant of proportionality. An empirical study has been carried out through simulation studies and real data application to ascertain the efficiency of the proposed estimators over other existing estimators considered in the study; and the results reveal that some members of the proposed estimators outperform the existing estimators, while some compete favorably with them.