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Monte Carlo Methods and Applications

Managing Editor: Sabelfeld, Karl K.

Editorial Board: Binder, Kurt / Bouleau, Nicolas / Chorin, Alexandre J. / Dimov, Ivan / Dubus, Alain / Egorov, Alexander D. / Ermakov, Sergei M. / Halton, John H. / Heinrich, Stefan / Kalos, Malvin H. / Lepingle, D. / Makarov, Roman / Mascagni, Michael / Mathe, Peter / Niederreiter, Harald / Platen, Eckhard / Sawford, Brian R. / Schmid, Wolfgang Ch. / Schoenmakers, John / Simonov, Nikolai A. / Sobol, Ilya M. / Spanier, Jerry / Talay, Denis

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Volume 18, Issue 2 (Jan 2012)


On the population median estimation using robust extreme ranked set sampling

Amer Ibrahim Al-Omari / Amjad D. Al-Nasser
Published Online: 2012-05-15 | DOI: https://doi.org/10.1515/mcma-2012-0002


In this paper, the robust extreme ranked set sampling (RERSS) scheme is considered for estimating the population median. The RERSS is compared with the simple random sampling (SRS), ranked set sampling (RSS) and extreme ranked set sampling (ERSS) schemes. A Monte Carlo simulation study is used to study the performance of the median estimator. It is found that RERSS estimators are unbiased of the population median when the underlying distribution is symmetric. Also, in terms of the efficiency criterion; the median estimator based on RERSS is more efficient than the median estimators based on SRS, ERSS, and RSS for symmetric and asymmetric distributions considered in this study. For asymmetric distributions, the RERSS estimators have a smaller bias.

Keywords: Ranked set sampling; robust extreme ranked set sampling; efficiency; Monte Carlo simulation

About the article

Received: 2011-04-25

Accepted: 2012-01-24

Published Online: 2012-05-15

Published in Print: 2012-06-01

Citation Information: Monte Carlo Methods and Applications, ISSN (Online) 1569-3961, ISSN (Print) 0929-9629, DOI: https://doi.org/10.1515/mcma-2012-0002.

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© 2012 by Walter de Gruyter Berlin Boston. Copyright Clearance Center

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