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Journals / Hacettepe Journal of Mathematics and Statistics / 2026 / Volume: 47 Issue: 3

Distribution function estimation using concomitant-based ranked set sampling

Ehsan Zamanzade
M. Mahdizadeh
Pages
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Publication Date
2018-06-01
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is shown by simulation study that the alternative estimator can be considerably more efficient than the standard one, especially when the rankings are perfect.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Ranked set sampling (RSS) is a data collection method designed to exploit auxiliary ranking information.
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Key Findings & Data:In this paper, a new estimator of distribution function is proposed when RSS is done by using a con comitant variable.
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Conclusion & Impact:It is shown by simulation study that the alternative estimator can be considerably more efficient than the standard one, especially when the rankings are perfect.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Relative efficiency · Concomitant variable · Judgment ranking

Abstract

Ranked set sampling (RSS) is a data collection method designed to exploit auxiliary ranking information. In this paper, a new estimator of distribution function is proposed when RSS is done by using a con comitant variable. It is shown by simulation study that the alternative estimator can be considerably more efficient than the standard one, especially when the rankings are perfect.

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Cite This Article

Zamanzade, E., Mahdizadeh, M. (2018). Distribution function estimation using concomitant-based ranked set sampling. Hacettepe Journal of Mathematics and Statistics, Volume: 47 Issue: 3. https://ajindex.com/articles/1402941

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