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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
An empirical study is also car-ried out to judge the merits of proposed estimators
📌 3 Key Takeaways
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Objective & Hypothesis:In this article, we suggest some regression-type estimators for the esti-mation of nite population variance using multi-variate auxiliary infor-mation under multi-phase sampling schemes when measurement error(ME) contaminates the study variable.
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Key Findings & Data:In this article, we suggest some regression-type estimators for the esti-mation of nite population variance using multi-variate auxiliary infor-mation under multi-phase sampling schemes when measurement error(ME) contaminates the study variable.
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Conclusion & Impact:An empirical study is also car-ried out to judge the merits of proposed estimators
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Multi-phase sampling · measurement error · mean square error
Abstract
In this article, we suggest some regression-type estimators for the esti-mation of nite population variance using multi-variate auxiliary infor-mation under multi-phase sampling schemes when measurement error(ME) contaminates the study variable. An empirical study is also car-ried out to judge the merits of proposed estimators
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Cite This Article
Shabbir, J., Masood, S. (2022). Generalized multi-phase regression-type estimators under the effect of measuemnent error to estimate the population variance. Hacettepe Journal of Mathematics and Statistics, Cilt: 45 - Sayı: 4, 1297–1306. https://ajindex.com/articles/530061