Ratio-to-Regression Estimator in Successive Sampling Using One Auxiliary Variable

Abstract

The problem of estimation of finite population mean on the current occasion based on the samples selected over two occasions has been considered. In this paper, first a chain ratio-to-regression estimator was proposed to estimate the population mean on the current occasion in two-occasion successive (rotation) sampling using only the matched part and one auxiliary variable, which is available in both the occasions. The bias and mean square error of the proposed estimator is obtained. We proposed another estimator, which is a linear combination of the means of the matched and unmatched portion of the sample on the second occasion. The bias and mean square error of this combined estimator is also obtained. The optimum mean square error of this combined estimator was compared with (i) the optimum mean square error of the estimator proposed by Singh (2005) (ii) mean per unit estimator and (iii) combined estimator suggested by Cochran (1977) when no auxiliary information is used on any occasion. Comparisons are made both analytically as well as empirically by using real life data.

Improved Rotation Patterns Using Two Auxiliary Variables in Successive Sampling

“…Utilizing the auxiliary information on both the occasions Feng and Zou (1997), Biradar and Singh (2001), Singh (2005) and Singh and Karna (2009 a, b) proposed ratio and regression type estimators for estimating the population mean on the current (second) occasion in two-occasion successive sampling. More recently, the contributions of Ralte and Das (2015), Singh and Pal (2015), Karna and Nath (2016) and Beevi and Chandran (2017) established beneficial results by using the auxiliary variables on both the occasions.…”