In simulation study the proposed estimators are more efficient as compared to other competitor estimators using Percentage Relative Efficiency (PRE), Percentage Relative Bias (PRB) and Percentage Relative Root Mean Square Error (PRRMSE).
📌 3 Key Takeaways
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Objective & Hypothesis:Stratified ranked set sampling (StRSS) combines the advantages of stratification and ranked set sampling (RSS).
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Key Findings & Data:In this paper, we propose several unbiased ratio type estimators using StRSS, when population mean of the auxiliary variable is known.
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Conclusion & Impact:In simulation study the proposed estimators are more efficient as compared to other competitor estimators using Percentage Relative Efficiency (PRE), Percentage Relative Bias (PRB) and Percentage Relative Root Mean Square Error (PRRMSE).
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Stratified ranked set sampling · ratio estimators · relative efficiency
Abstract
Stratified ranked set sampling (StRSS) combines the advantages of stratification and ranked set sampling (RSS). In this paper, we propose several unbiased ratio type estimators using StRSS, when population mean of the auxiliary variable is known. The variances of the proposedunbiased ratio-type estimators are obtained to first degree of approximation. In simulation study the proposed estimators are more efficient as compared to other competitor estimators using Percentage Relative Efficiency (PRE), Percentage Relative Bias (PRB) and Percentage Relative Root Mean Square Error (PRRMSE).
GUPTA, S., SHABBİR, J., KHAN, L. (2022). Unbiased ratio estimators of the mean in stratified ranked set sampling. Hacettepe Journal of Mathematics and Statistics, Cilt: 46 - Sayı: 6, 1151–1158. https://ajindex.com/articles/529976
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