AI Summary & Key Takeaways
AI InsightFrom theoretical and empirical study, We observed that the proposed estimators perform better as compared to the usual ratio and the existing estimators consider in this study.
📌 3 Key Takeaways
Abstract
Estimation of population mean is of prime concern in many studies and ratio estimators are popular choices for it. It is a common practice to use conventional measures of location to develop ratio estimators using information on auxiliary variables. In this article, we propose a class of ratio estimators for a finite population mean using information on two auxiliary variables with the help of some non-conventional location measures. We have incorporated tri-mean, Hodges-Lehmann, mid-range and decile mean of the two auxiliary variables to serve the purpose. The properties associated with the proposed class of ratio estimators are evaluated using mean square error. We have presented efficiency comparisons of the proposed class of ratio estimators with other existing estimators under the optimal conditions. An empirical study is also included for illustration and verication purposes. From theoretical and empirical study, We observed that the proposed estimators perform better as compared to the usual ratio and the existing estimators consider in this study.