Improved Estimation of Finite Population Variance Using Auxiliary Information

Abstract

This article addresses the problem of estimating of finite population variance using auxiliary information in simple random sampling. A ratio-cum-difference type class of estimators for population variance has been suggested with its properties under large sample approximation. It has been shown that the suggested class of estimators is more efficient than usual unbiased, difference, Das and Tripathi (1978), Isaki (1983), Singh et al. (1988), Kadilar and Cingi (2006), and other estimators/classes of estimators. In addition, we support this theoretical result with the aid of a empirical study.

Keywords:

• Auxiliary variable
• Bias
• Mean squared error
• Study variable
• Variance

Mathematics Subject Classification:

• 62D05

Acknowledgments

The authors are thankful to the Editor-in-Chief Professor N. Balakrishnan and two anonymous referees for their constructive comments and suggestions for the improvement of this article.

Notes

• where ρ is the correlation coefficient between x and y.

• (i = 1, 2; j = 1, 2,…, 9)