A New Improved Class of Estimators For The Population Variance

In this paper by utilizing the information on the population mean of auxiliary variable, we proposed a new improved class of estimators for the population variance of the study variable. The large sample properties of proposed estimator have been studied up to the first order of approximation that is the mathematical expressions for the bias and mean square error (MSE) of the proposed class of estimators have been obtained up to the first order of approximation. The optimum values of the characterizing scalars, which minimize the MSE of proposed estimator, have been obtained. For these optimum values of characterizing scalars, the minimum MSE of proposed estimator has been obtained. Further a numerical study is also carried out. It has been shown that the proposed estimator is more efficient than sample variance, traditional ratio estimator due to Isaki [3], Singh et al. [6] exponential ratio estimator, estimator based on Kadilar and Cingi [4] ratio estimator, Upadhyaya and Singh [8] estimator and Asghar et al. [1] estimator for the population variance under optimum conditions