An improved class of estimators for finite population variance

Authors : Mazhar YAQUB, Javid SHABBİR

Pages : 1641-1660

View : 14 | Download : 6

Publication Date : 2016-10-01

Article Type : Research Paper

Abstract :We propose an improved class of estimators in estimating the finite population variance, using the auxiliary information. The expressions for the bias and mean squared error of the proposed class of estimators are derived up to the first order of approximation. Some estimators are also derived from a proposed class by allocating the suitable values of known parameters and identified as particular members of the proposed class of estimators. A numerical study is carried out to demonstrate performances of the estimators. It is observed that the proposed class of estimators is more efficient than the usual sample mean estimator, the regression estimator suggested by Isaki insert ignore into journalissuearticles values(1983);, Shabbir and Gupta insert ignore into journalissuearticles values(2007);, Singh and Solanki insert ignore into journalissuearticles values(2013b);, Yadav et al. insert ignore into journalissuearticles values(2013);, Yadav and Kadilar insert ignore into journalissuearticles values(2014); and Singh and Malik insert ignore into journalissuearticles values(2014); estimators.
Keywords : Auxiliary variable, bias, mean squared error, population variance, percentage relative efficiency