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On the population median estimation using robust extreme ranked set sampling

Author

Listed:
  • Al-Omari Amer Ibrahim

    (Department of Mathematics, Al al-Bayt University, Mafraq, Jordan)

  • Al-Nasser Amjad D.

    (College of Business Administration, University of Dubai, Dubai, UAE)

Abstract

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.

Suggested Citation

  • Al-Omari Amer Ibrahim & Al-Nasser Amjad D., 2012. "On the population median estimation using robust extreme ranked set sampling," Monte Carlo Methods and Applications, De Gruyter, vol. 18(2), pages 109-118, January.
    • Handle: RePEc:bpj:mcmeap:v:18:y:2012:i:2:p:109-118:n:1
    DOI: 10.1515/mcma-2012-0002
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