Enhanced generalized class of estimators for estimation of population median using auxiliary information

Although many researchers have developed reliable methods for estimating the unknown population parameters, these methods fail miserably in the presence of outliers. The goal of this research is to provide an improved generalized class of estimators for estimation of population median using auxiliary information. Estimates of the first order are utilized to compute the bias and mean squared error. To check efficiency of the developed estimators compared to existing estimators, we used real life data sets. By putting different choices of r , s and Ϣ , we obtain seven enhanced estimators form our generalized class of estimators. We obtain the optimum values in the suggested class of estimators, and then achieve minimum mean squared expressions. According to both theoretical and numerical findings, our newly suggested class of estimators outperforms the alternatives. Eventually, the computational results are quite advanced and provide more improvement than the existing estimators. Thus, considering both theoretical and empirical results, we highly advise the proposed classes of estimators for the future use. 62D05.