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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Analytical and numerical results show that at optimal conditions, the proposed ratio estimators are always more efficient than the regression estimator and some existing estimators under review.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper proposes, with justification, two exponential ratio estimators of population mean in simple random sampling without replacement.
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Key Findings & Data:Their biases and mean squared error are derived and compared with existing related ratio estimators.
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Conclusion & Impact:Analytical and numerical results show that at optimal conditions, the proposed ratio estimators are always more efficient than the regression estimator and some existing estimators under review.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Exponential ratio estimators · mean squared error · optimal conditions
Abstract
This paper proposes, with justification, two exponential ratio estimators of population mean in simple random sampling without replacement. Their biases and mean squared error are derived and compared with existing related ratio estimators. Analytical and numerical results show that at optimal conditions, the proposed ratio estimators are always more efficient than the regression estimator and some existing estimators under review.
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Cite This Article
John, E. E., Inyang, E. E. (2022). Efficient exponential ratio estimator for estimating the population mean in simple random sampling. Hacettepe Journal of Mathematics and Statistics, Cilt: 44 - Sayı: 3, 689–705. https://ajindex.com/articles/530245