Generalized Class of Estimators for Median Estimation Using Auxiliary Information

Abstract

In this article, we propose a comprehensive class of estimator for estimation of population median under simple random sampling. To boost the efficiency of an estimator we utilize the auxiliary information. The numerical expression of the bias and mean squared error are consequent up to the first order of approximation. The proposed and existing estimators have been evaluated via real data sets and their performances were assessed using measurements of minimal mean square error and maximum percentage relative efficiency. The study showed that compared to some adopted existing estimators in this study, the proposed class of estimators performed better and efficient. We also visualize all the estimators using results of MSE and PRE. According to the results, the suggested estimator is superior to the adopted existing estimators. The significance and potential applications of our proposed class of estimators are highlighted by these results.