An Empirical and Simulation-Based Evaluation of Existing Class Estimators in Two-Occasion Successive Sampling
Keywords:
uccessive sampling, Population mean, Estimators, Efficiency, Simulation study, Correlation strengthAbstract
This study presents an empirical and simulation-based comparison of four established estimators for estimating the population mean in two-occasion successive sampling. Artificial populations have been generated under varying correlation structures (strong, moderate, and weak) and different sample sizes to evaluate estimator their performances using percent relative efficiency (PRE) and the optimum replacement policy. The results reveal that estimators’ efficiencies increase with increase in correlation strength and sample size. Real-data applications supported the simulation outcomes, confirming the superior and consistent performance of some estimators over others across multiple populations. Overall, no single estimator dominated across all conditions, emphasizing that the choice of estimator should depend on the expected correlation structure and sampling design.Downloads
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2025-10-25
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Authors retain copyright and grant the journal the right of first publication. Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0), permitting unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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An Empirical and Simulation-Based Evaluation of Existing Class Estimators in Two-Occasion Successive Sampling. (2025). Applied Science, Computing, and Energy, 3(3), 471-481. https://cemrj.com/index.php/volumes/article/view/147
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