Some Estimators for the Population Mean Using Auxiliary Information Under Ranked Set Sampling

Some Estimators for the Population Mean Using Auxiliary Information Under Ranked Set Sampling

Some estimators for a finite population mean under two-stage sampling using multivariate auxiliary information

In this paper, we have derived difference, regression and predictor estimators for two-stage sampling with unequal first-stage units using multivariate auxiliary information. The mean square error of these estimators have been derived. A numerical illustration is given for finding the relative comparisons. 2003 Elsevier Inc. All rights reserved.

A class of Hartley-Ross type Unbiased estimators for Population Mean using Ranked Set Sampling

In this paper, we propose a class of Hartley-Ross type unbiased estimators for estimating the finite population mean of the study variable under ranked set sampling (RSS), when population mean of the auxiliary variable is known. The variances of the proposed class of unbiased estimators are obtained to first degree of approximation. Both theoretically and numerically, the proposed estimators ar...

An Efficient Class of Estimators for the Finite Population Mean in Ranked Set Sampling

In this paper, we propose a class of estimators for estimating the finite population mean of the study variable under Ranked Set Sampling (RSS) when population mean of the auxiliary variable is known. The bias and Mean Squared Error (MSE) of the proposed class of estimators are obtained to first degree of approximation. It is identified that the proposed class of estimators is more efficient as...

Some Estimators for a Finite Population Mean by Using Estimated Multi-auxiliary Information

The objective of this paper is to derived difference, regression and predictive estimators for two stage sampling by using double sampling technique when multi-auxiliary information is unavailable. The mean square errors for these estimators have been derived. A numerical study is given for finding the relative comparison.