A Joint Calibration Estimator of Population Total Under Minimum Entropy Distance Function Based on Dual Frame Surveys

Abstract

The concept of dual frame-based estimators has been already developed in sample surveys. These dual frame estimators are theoretically optimal in some cases but difficult to apply in practice, while the others are generally applicable but may have larger variances. In this chapter, we propose Joint Calibration Estimator (JCE) under minimum entropy distance function for the dual frame surveys. The proposed estimator has smaller bias and considerable decrement in variance under Lahiri–Midzuno design as compared to Simple Random Sampling Without Replacement when sample size increases. In addition, we obtain the optimal weights along with its sensitive weighing interval for the combined JCE under non-overlapping frames for which it is more efficient than individual frame-based estimator. © Springer Nature Singapore Pte Ltd 2020.