Abstract

In a case of bivariate finite populations where the mean X of an auxiliary characteristics x is known, it is customary to define ratio, regression, product and difference estimators for estimating mean Y of a principal variable y . It is well known that for large samples the mean squared error of regression estimator is smaller than those of other estimators mentioned above. In this paper, we make a search for some estimators whose MSE may be smaller than that of regression estimator. For estimating Y we have considered several estimators of the form d = (1− w) yrg + wt , where yrg is well known regression estimator, w is a suitably chosen weight and t is a function of y and x values in the sample. We have obtained optimum choices of weights w and corresponding minimum mean squared errors. The results are illustrated for bivariate normal populations. The relative efficiencies of the proposed estimators compared to that of regression estimator have been obtained for a natural population data.

Author: Vyas Dubey and T.P. Tripathi

Received on: August, 2004

Accepted on: September, 2006