An Effective Class of Ratio Estimators of Population Variance in Survey Sampling

Abstract

Under the simple random sampling without replacement (SRSWOR) scheme, we have proposed a generalized ratio-type estimator to more effectively estimate the population variance while incorporating auxiliary information. It provides the bias and Mean Square Error (MSE) mathematical equations for a first-order approximation. Efficiency and alternative estimators are theoretically contrasted. These findings are supported by an empirical study that uses a real population dataset to show that the suggested estimator performs better than the alternatives. The proposed estimator can be used to many fields for better estimation of population variance.