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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
It is shown that the proposed class of estimators is more efficient than [13] and [17] classes of estimators
📌 3 Key Takeaways
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Objective & Hypothesis:This paper addresses the problem of non-response when estimating the population mean of the variable of interest.
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Key Findings & Data:A general class of estimators is suggested for the unknown population mean of the study variable.
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Conclusion & Impact:It is shown that the proposed class of estimators is more efficient than [13] and [17] classes of estimators
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Auxiliary information · Multi-phase sampling · Non-response
Abstract
This paper addresses the problem of non-response when estimating the population mean of the variable of interest. A general class of estimators is suggested for the unknown population mean of the study variable. Two vectors of the auxiliary information under multi-phase sampling scheme are used in presence of non response. The asymptotic variance of the proposed class is determined and compared with some other existing estimators theoretically and numerically. It is shown that the proposed class of estimators is more efficient than [13] and [17] classes of estimators
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Cite This Article
Riaz, S., Diana, G., Shabbir, J. (2014). A general class of estimators for the population mean using multi-phase sampling with the non-respondents. Hacettepe Journal of Mathematics and Statistics, Volume: 43 Issue: 3. https://ajindex.com/articles/1403404