✨
AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
We have compared the proposed estimator with usual unbiased estimator and usual double sampling ratio estimator and shown that the proposed estimator is better than usual unbiased estimator and other existing estimators under some realistic conditions to two-phase sampling.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:This paper presents a double sampling version of Yadav and Kadilar (2013) estimator along with its properties under large sample approximation.
📊
Key Findings & Data:Cost aspect is also discussed.
💡
Conclusion & Impact:We have compared the proposed estimator with usual unbiased estimator and usual double sampling ratio estimator and shown that the proposed estimator is better than usual unbiased estimator and other existing estimators under some realistic conditions to two-phase sampling.
🔬Design:Research Article
📍Scope:2013 Verileri
⚙️Variables:Auxiliary variable · Bias · Efficiency
Abstract
This paper presents a double sampling version of Yadav and Kadilar (2013) estimator along with its properties under large sample approximation. Cost aspect is also discussed. We have compared the proposed estimator with usual unbiased estimator and usual double sampling ratio estimator and shown that the proposed estimator is better than usual unbiased estimator and other existing estimators under some realistic conditions to two-phase sampling.
🔖
Cite This Article
Singh, H. P., Goli, R., Tarray, T. A. (2015). Study of Yadav and Kadilar’s improved exponential type ratio estimator of population variance in two-phase sampling. Hacettepe Journal of Mathematics and Statistics, Volume: 44 Issue: 5. https://ajindex.com/articles/1403252