Estimation of the Population Mean Using Paired Ranked Set Sampling

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...

Estimation of Variance of Normal Distribution using Ranked Set Sampling

Introduction     In some biological, environmental or ecological studies, there are situations in which obtaining exact measurements of sample units are much harder than ranking them in a set of small size without referring to their precise values. In these situations, ranked set sampling (RSS), proposed by McIntyre (1952), can be regarded as an alternative to the usual simple random sampling ...

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

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...

Improved Estimation from Ranked Set Sampling

Ranked set sampling is used when the measurement or quantification of units of the variable under study is difficult but the ranking of units of sets of small sizes can be done easily by an inexpensive method. Dell and Clutter (1972) showed that the sample mean based on ranked set sample is more efficient than the sample mean based on simple random sample with replacement sampling procedure for...