On Regression Estimators Using Extreme Ranked Set Samples

Extreme ranked set sampling: A comparison with regression and ranked set sampling estimators

Ranked set sampling (RSS) assumed perfect ranking i.e. there will be no errors in ranking the units with respect to the variable of interest. In fact for most practical applications, it is not easy to rank the units without errors in ranking. There will be a loss in efficiency, i.e. RSS will give a larger variance due to the errors in ranking the units. To reduce the errors in ranking in estima...

Improved entropy based test of uniformity using ranked set samples

Ranked set sampling (RSS) is known to be superior to the traditional simple random sampling (SRS) in the sense that it often leads to more efficient inference procedures. Basic version of RSS has been extensively modified to come up with schemes resulting in more accurate estimators of the population attributes. Multistage ranked set sampling (MSRSS) is such a variation surpassing RSS. Entropy ...

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...