Some Efficient Exponential Ratio Type Estimators in Adaptive Cluster Sampling
Rajesh Singh
Department of Statistics, Institute of Science, Banaras Hindu University, Varanasi, India.
Sat N. Gupta
Department of Mathematics and Statistics, the University of North Carolina at Greensboro, North Carolina, USA.
Rohan Mishra *
Department of Statistics, Institute of Science, Banaras Hindu University, Varanasi, India.
*Author to whom correspondence should be addressed.
Abstract
In this paper three efficient exponential ratio type estimators of finite population mean in the Adaptive Cluster Sampling design have been proposed using one known auxiliary variable. The expressions of bias and mean squared error of the proposed estimators are derived up to the first order of approximation. A simulation study has been conducted on two different populations to examine the performance of the proposed estimator over similar existing estimators in the Adaptive Cluster Sampling design. The simulation study showed that the proposed estimators perform better than other related estimators discussed in this article.
Keywords: Adaptive cluster sampling, simulation, exponential estimator, within-network variance, ratio estimator