Some Improved Combined Estimators of Population Mean in Stratified Ranked Set Sampling

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

Unbiased Ratio Estimators of the Mean in Stratified Ranked Set Sampling

Stratified ranked set sampling (StRSS) combines the advantages of stratification and ranked set sampling (RSS). In this paper, we propose several unbiased ratio type estimators using StRSS, when population mean of the auxiliary variable is known. The variances of the proposed unbiased ratio-type estimators are obtained to first degree of approximation. In simulation study the proposed estimator...

Design Based Estimation of Finite Population Mean in Ranked Set Sampling

Abstract. This Article introduce method of ranked set sampling with replacement (RSSWR) in finite population and express how to computing samples of inclusion probability for this method. The Horvitz-Thompson and Hansen-Hurwtz estimators using auxiliary variables introduce for this design and use 2011-12 Urban Households Income and Income and Expenditure survey data, gathered for Tehran by stat...

Stratified Median Ranked Set Sampling: Optimum and Proportional Allocations

In this paper, for the Stratified Median Ranked Set Sampling (SMRSS), proposed by Ibrahim et al. (2010), we examine the proportional and optimum sample allocations that are two well-known methods for sample allocation in stratified sampling. We show that the variances of the mean estimators of a symmetric population in SMRSS using optimum and proportional allocations to strata are smaller than ...

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...