Estimation of Population Variance Using the Coe cient of Kurtosis and Median of an Auxiliary Variable Under Simple Random Sampling

Abstract

In this study we have proposed a modi ed ratio type estimator for population variance of the study variable y under simple random sampling without replacement making use of coe cient of kurtosis and median of an auxiliary variable x. The estimator's properties have been derived up to rst order of Taylor's series expansion. The e ciency conditions are derived theoretically under which the proposed estimator performs better than existing estimators. Empirical studies have been done using real populations to demonstrate the performance of the developed estimator in comparison with the existing estimators. The proposed estimator as illustrated by the empirical studies performs better than the existing estimators under some speci ed conditions i.e. it has the smallest Mean Squared Error and the highest Percentage Relative E ciency. The proposed estimator is therefore suitable to be applied to situations in which the variable of interest has a positive correlation with the auxiliary variable.