A Class of Hartley-Ross-Type Estimators for Population Mean in Adaptive and Stratified Adaptive Cluster Sampling
In this article, we propose a class of Hartley-Ross-type unbiased estimators of finite population mean under adaptive cluster sampling and stratified adaptive cluster sampling. The estimators utilize information on known parameters of the auxiliary variable. The variances of proposed class of unbiased estimators are obtained up to first degree of approximation. Proposed estimators are more efficient than the usual mean estimator, ratio, and modified ratio estimators in adaptive cluster sampling and stratified adaptive cluster sampling under certain realistic conditions. Simulation study considering different populations is carried out to support the theoretical findings.
KeywordsAdaptive cluster sampling Auxiliary variable Hartley-Ross Bias Mean square error
The authors are thankful to the editor and anonymous referees for their valuable suggestions, which helped to improve the article.