Abstract
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 compared to [1] estimator and several other estimators. A simulation study is carried out to judge the performances of the estimators.
Highlights
The problem of estimation in the finite population mean has been widely considered by many authors in different sampling designs
There may be a situation when the variable of interest cannot be measured or is very expensive to do so, but it can be ranked at no cost or at very little cost. In view of this situation, [2] introduced the Ranked Set Sampling (RSS) procedure. [3] proved the mathematical theory that the sample mean under RSS was an unbiased estimator of the finite population mean and more precise than the sample mean estimator under simple random sampling (SRS)
In [6], the ranking of elements was done on basis of the auxiliary variable instead of judgment. [1] suggested an estimator for population mean and ranking of the elements was observed on basis of the auxiliary variable. [7] had suggested a class of Hartley-Ross type unbiased estimators in RSS. [8] had proposed unbiased estimators in RSS and stratified ranked set sampling
Summary
The problem of estimation in the finite population mean has been widely considered by many authors in different sampling designs. There may be a situation when the variable of interest cannot be measured or is very expensive to do so, but it can be ranked at no cost or at very little cost In view of this situation, [2] introduced the Ranked Set Sampling (RSS) procedure. In RSS, perfect ranking of elements was considered by [2] and [3] for estimation of population mean. (2016) An Efficient Class of Estimators for the Finite Population Mean in Ranked Set Sampling. [1] suggested an estimator for population mean and ranking of the elements was observed on basis of the auxiliary variable. [8] had proposed unbiased estimators in RSS and stratified ranked set sampling. We suggest a class of estimators for the population mean, using known population mean of the auxiliary variable in RSS.
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