Optimal calibrated weights while minimizing a variance function

Abstract

The current investigation considers the query of assessment of estimators of population mean through calibration technique. We proposed new multi-variable calibrated estimator of mean in stratified sampling by employing the g multiple auxiliary variables. We introduce new variance function of the study variable in replacement to chi-square distance function under the assumption of known population variance of the study variable by some previous knowledge or past study as in case of Neyman allocation. It has been shown through simulation and numerical studies that the resultant estimators are much proficient than the usual combined mean estimator as well as combined ratio and regression estimators.

Keywords:

• Design weights
• multivariate calibrated weights
• Lagrangian multipliers
• stratified random sampling
• proportional allocation
• percentage relative efficiency
• simulation study