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

This study proposes a new generalized estimator based on auxiliary information for the estimation of population variance in the presence and absence of measurement error. Approximate expressions of bias and mean square error of the proposed generalized estimator are derived up to the first order. Several new and existing estimators are found to be special cases of the proposed estimator and expressed on different values of optimized and generalized constants. The proposed estimator is compared mathematically with a number of existing estimators under certain conditions. The performance of the proposed estimator is evaluated through simulation and real-data application under different sample sizes. It was observed that the proposed estimator performed better than other competing estimators in the presence and absence of measurement errors.