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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
It is demonstrated that the proposed estimators are more efficient than others.
📌 3 Key Takeaways
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Objective & Hypothesis:Use of auxiliary variables is very common in estimating various population parameters.
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Key Findings & Data:Thevariance expressions of the suggested estimators are compared with usual unbiased estimator, Swain's (1964) ratio estimator and Shukla's(1971) product type estimator.
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Conclusion & Impact:It is demonstrated that the proposed estimators are more efficient than others.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Systematic sampling · Ratio estimator · Product estimator
Abstract
Use of auxiliary variables is very common in estimating various population parameters. In this study, we suggest a class of unbiased linear estimators for estimating the population mean of the study variate y using information on the auxiliary variate x in systematic sampling. Thevariance expressions of the suggested estimators are compared with usual unbiased estimator, Swain's (1964) ratio estimator and Shukla's(1971) product type estimator. It is demonstrated that the proposed estimators are more efficient than others.
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Cite This Article
KOÇYİĞİT, E. G., ÇINGI, H. (2022). A new class of unbiased linear estimators in systematic sampling. Hacettepe Journal of Mathematics and Statistics, Cilt: 46 - Sayı: 2, 315–323. https://ajindex.com/articles/530000