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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
It is shown that the proposed estimator is more efficient as compared to all other competitor estimators under RSS scheme.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper proposed Hartley-Ross (HR) unbiased estimator of the finite population mean using two auxiliary variables in ranked set sampling (RSS).
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Key Findings & Data:The variance of the proposed unbiased estimator is obtained to first degree of approximation.
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Conclusion & Impact:It is shown that the proposed estimator is more efficient as compared to all other competitor estimators under RSS scheme.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Ranked set sampling · unbiased estimator · efficiency
Abstract
This paper proposed Hartley-Ross (HR) unbiased estimator of the finite population mean using two auxiliary variables in ranked set sampling (RSS). The variance of the proposed unbiased estimator is obtained to first degree of approximation. Comparison among the proposed and some existing estimators are made both theoretically and through simulation study. It is shown that the proposed estimator is more efficient as compared to all other competitor estimators under RSS scheme.
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Cite This Article
KHAN, L., SHABBİR, J. (2017). HARTLEY-ROSS UNBIASED ESTIMATOR OF POPULATION MEAN IN RANKED SET SAMPLING USING TWO AUXILIARY VARIABLES. Gazi University Journal of Science, Cilt: 30 - Sayı: 2, 195–203. https://ajindex.com/articles/254618