Generalized Exponential-Cum-Exponential Estimator in Adaptive Cluster Sampling

Abstract

In this paper, a generalized exponential-cum-exponential estimator is proposed utilizing the two auxiliary variables based on average values of the networks in adaptive cluster sampling. The exponential ratio-cum- exponential ratio, exponential product-cum- exponential product, exponential ratio-cum- exponential product and exponential product-cum- exponential ratio type estimators are the special cases of proposed estimator using simple random sampling without replacement in adaptive cluster sampling. The expressions for the mean square error and bias of the proposed estimator have been derived. The class of special cases of proposed estimator may be used for estimating the finite population mean and comparable with estimators in case of high correlation but also useful when the correlation between study variable and auxiliary variables is low in the adaptive cluster sampling. The simulation studies have been carried out to demonstrate and compare the efficiencies of the estimators. It is shown that the proposed estimators are more efficient as compared to the mean per unit estimator in adaptive cluster sampling, modified ratio and modified product, exponential ratio and exponential product estimators in adaptive cluster sampling, under given conditions.