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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
The simulation study shows that new calibration estimator is highly efficient than the existing estimator.
📌 3 Key Takeaways
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Objective & Hypothesis:In the present article, we consider a new calibration estimator of the population mean in the stratified double sampling.
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Key Findings & Data:We get more efficient calibration estimator using new calibration weights compared to the straight estimator.
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Conclusion & Impact:The simulation study shows that new calibration estimator is highly efficient than the existing estimator.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Calibration · Auxiliary information · Stratified double sampling
Abstract
In the present article, we consider a new calibration estimator of the population mean in the stratified double sampling. We get more efficient calibration estimator using new calibration weights compared to the straight estimator. In addition, the estimators derived are analyzed for different populations by a simulation study. The simulation study shows that new calibration estimator is highly efficient than the existing estimator.
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Cite This Article
KADILAR, C., KOYUNCU, N. (2022). A new calibration estimator in stratified double sampling. Hacettepe Journal of Mathematics and Statistics, Cilt: 43 - Sayı: 2, 337–346. https://ajindex.com/articles/530317