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


Mean squared error, Bias, Auxiliary information, ratio-cum-product estimators


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.





Pakistan Journal of Statistics and Operation Research; Vol. 11 No. 2, 2015

Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810