Also, the performance of the proposed class of estimators is shown theoretically and these theoretical conditions are, then, verified numerically by taking three natural populations under which the proposed class of estimators performed better than other competing estimators.
📌 3 Key Takeaways
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Objective & Hypothesis:This paper presents a class of ratio-type estimators for the evaluation of finite population mean under maximum and minimum values by using knowledge of the auxiliary variable.
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Key Findings & Data:The properties of the proposed estimators in terms of biases and mean square errors are derived up to first order of approximation.
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Conclusion & Impact:Also, the performance of the proposed class of estimators is shown theoretically and these theoretical conditions are, then, verified numerically by taking three natural populations under which the proposed class of estimators performed better than other competing estimators.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Study variable · Auxiliary variable · Ratio estimators
Abstract
This paper presents a class of ratio-type estimators for the evaluation of finite population mean under maximum and minimum values by using knowledge of the auxiliary variable. The properties of the proposed estimators in terms of biases and mean square errors are derived up to first order of approximation. Also, the performance of the proposed class of estimators is shown theoretically and these theoretical conditions are, then, verified numerically by taking three natural populations under which the proposed class of estimators performed better than other competing estimators.
Khan, M., Ullah, S., Al-hossain, A. Y., Bashir, N. (2015). Improved ratio-type estimators using maximum and minimum values under simple random sampling scheme. Hacettepe Journal of Mathematics and Statistics, Volume: 44 Issue: 4. https://ajindex.com/articles/1403270