A Family of Estimators for Estimating Population Variance Using Auxiliary Information in Sample Survey

Abstract

In this article an efficient class of estimators for estimating finite population variance has been proposed using auxiliary information in simple random sampling. The bias and mean squared error of the proposed estimator is obtained up to the first degree of approximation. It has been shown that the proposed estimator is more efficient than usual unbiased estimator, Isaki (J. Am. Stat. Assoc.78:117-123, 1983), Kadilar and Cingi (Appl. Math. & Comput., 173, 1047-1059, 2006) and Upadhyaya and Singh (Vikram Math. J. 19, 14-17, 1999a). To judge the merits of the proposed estimator, we consider one numerical example.