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Cost-effective and efficient sampling methods are of main concern in many social, biological and environmental studies. In this article, an efficient sampling scheme, named manipulation-based ranked set sampling (MBRSS) scheme is introduced with its properties for estimating population mean and median. The MBRSS is a mixture of simple random sampling (SRS), ranked set sampling (RSS) and median ranked set sampling (MRSS) schemes and is applicable in the situation when ordinary RSS cannot be conducted. It is shown that the proposed scheme provides unbiased mean estimator provided underlying distribution is symmetric. For asymmetric distributions, a weighted mean is proposed, where optimal weights are computed using Shannon's entropy. Monte Carlo simulation is used to ascertain effectiveness of the proposed mean and median estimators in the presence of outliers. We also compared the efficiency of MBRSS and truncation-based ranked set sampling (TBRSS) scheme with respect to SRS under the situation of perfect and imperfect ranking i.e error in rankings with respect to variable of interest. It is observed, on the basis of theoretical and numerical studies that MBRSS is more efficient than SRS. Further, a real data set is used to illustrate the proposed MBRSS scheme. [ABSTRACT FROM AUTHOR]

In this paper, we propose a double robust truncation based ranked set sampling (DRTBRSS) for estimating population mean and median. It provides unbiased estimator of population mean under symmetric population and its efficiency dominates the efficiency of conventional estimators based on ranked set sampling (RSS), truncation based rank set sampling (TBRSS) and simple random sampling (SRS). Monte Carlo simulation, based on perfect ranking and ranking based on auxiliary variable, is used under symmetric and asymmetric distributions to evaluate effectiveness of the proposed sampling method. Further, a real data set is used to illustrate the proposed sampling design. [ABSTRACT FROM AUTHOR]