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Efficient estimation of cumulative distribution function using moving extreme ranked set sampling with application to reliability

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Abstract

In this article, we consider the problem of estimating cumulative distribution function (CDF) and a reliability parameter using moving extreme ranked set sampling (MERSS). Two different CDF estimators are described and compared with their competitors in simple random sampling (SRS) and ranked set sampling (RSS). It turns out the CDF estimators in MERSS can be more efficient than their competitors in SRS and RSS at a point in a particular tail of the distribution when the quality of rankings is sufficiently good. Motivated by this efficiency gain, we develop some estimators for the stress-strength probability using MERSS. The suggested estimators are then compared with their counterparts in the literature via Monte Carlo simulation. Finally, a real dataset is used to show the applicability of the developed procedures.

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Notes

  1. Access date: August 2019.

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Acknowledgements

The authors thank the reviewers for helpful comments that have resulted in an improved paper.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan, 81746-73441, Iran

    Ehsan Zamanzade

  2. Department of Statistics, Hakim Sabzevari University, P.O. Box 397, Sabzevar, Iran

    M. Mahdizadeh

  3. Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA

    Hani M. Samawi

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  1. Ehsan Zamanzade
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  2. M. Mahdizadeh
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  3. Hani M. Samawi
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Correspondence to Ehsan Zamanzade.

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Zamanzade, E., Mahdizadeh, M. & Samawi, H.M. Efficient estimation of cumulative distribution function using moving extreme ranked set sampling with application to reliability. AStA Adv Stat Anal 104, 485–502 (2020). https://doi.org/10.1007/s10182-020-00368-3

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  • DOI: https://doi.org/10.1007/s10182-020-00368-3

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