New ranked set sampling for estimating the population mean and variance

Year 2016, Volume: 45 Issue: 6, 1891 - 1905, 01.12.2016

Ehsan Zamanzade Amer İbrahim Al-omari

Abstract

The purpose of this study is to suggest a new modification of the usual
ranked set sampling (RSS) method, namely; neoteric ranked set sampling (NRSS) for estimating the population mean and variance. The
performances of the empirical mean and variance estimators based on
NRSS are compared with their counterparts in ranked set sampling and
simple random sampling (SRS) via Monte Carlo simulation. Simulation results indicate that the NRSS estimators perform much better
than their counterparts using RSS and SRS designs when the ranking
is perfect. When the ranking is imperfect, the NRSS estimators are
still superior to their counterparts in ranked set sampling and simple
random sampling methods. These findings show that the NRSS provides a uniform improvement over RSS without any additional costs.
Finally, an illustrative example of a real data is provided to show the
application of the new method in practice.

Keywords

Ranked set sampling, Imperfect rankings, Simple random sampling, Monte Carlo simulation

References

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