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

Ehsan Zamanzade (), M. Mahdizadeh () and Hani M. Samawi ()
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Ehsan Zamanzade: University of Isfahan
M. Mahdizadeh: Hakim Sabzevari University
Hani M. Samawi: Georgia Southern University

AStA Advances in Statistical Analysis, 2020, vol. 104, issue 3, No 6, 485-502

Abstract: 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.

Keywords: Judgment ranking; Ranked set sampling; Stress-strength probability; 62D05; 62F03 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:alstar:v:104:y:2020:i:3:d:10.1007_s10182-020-00368-3

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DOI: 10.1007/s10182-020-00368-3

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