Calibrated Estimator for Sensitive Variables under Stratified Random Sampling

Calibration sampling is a general tool to adjust the sampling weights and enhance the precision of the estimates. This technique is also helpful to reduce the non-response errors. In order to remove or minimize the biases produced by non-response errors and variances, the calibration technique is utilized. In this paper, Calibration technique is used to reduce the distance between the calibrated weights and the given distance measure. We propose new calibration estimators for estimating the population of a sensitive variable based on scrambled responses collected using some improved random response device and auxiliary information. This study is to propose some improved calibrated generalized estimators for estimation of population mean of a quantitative sensitive variable. The results show that the proposed estimator having an extra calibration constraint is more efficient.