Generalized Variance Estimator using Auxiliary Information in the Presence and Absence of Measurement Error

In this study, we have proposed a new generalized estimator using auxiliary information for the estimation of population variance in presence and absence of measurement error. The expressions approximate bias and mean square error of the proposed generalized estimator are derive up to the first-order. Several new and existing estimators are found as the special cases of proposed estimator and expressed on various values of optimizing and generalized constants. The proposed estimator is compared mathematically with some existing estimators under certain conditions. The performance of proposed estimator is observed by simulation and real data application under different sample sizes. It is observed that the proposed estimator performed well than other competing estimators in the presence and absence of measurement error.