A new procedure for variance estimation in simple random sampling using auxiliary information

In this article we have envisaged an efficient generalized class of estimators for finite population variance of the study variable in simple random sampling using information on an auxiliary variable. Asymptotic expressions of the bias and mean square error of the proposed class of estimators have been obtained. Asymptotic optimum estimator in the proposed class of estimators has been identified with its mean square error formula. We have shown that the proposed class of estimators is more efficient than the usual unbiased, difference, Das and Tripathi (Sankhya C 40:139–148, <CitationRef CitationID="CR5">1978</CitationRef>), Isaki (J. Am. Stat. Assoc. 78:117–123, <CitationRef CitationID="CR12">1983</CitationRef>), Singh et al. (Curr. Sci. 57:1331–1334, <CitationRef CitationID="CR44">1988</CitationRef>), Upadhyaya and Singh (Vikram Math. J. 19:14–17, <CitationRef CitationID="CR55">1999b</CitationRef>), Kadilar and Cingi (Appl. Math. Comput. 173:2, 1047–1059, <CitationRef CitationID="CR15">2006a</CitationRef>) and other estimators/classes of estimators. In the support of the theoretically results we have given an empirical study. Copyright Springer-Verlag 2013