On the Performance of Mean Estimators Under Ranked Set Sampling Techniques Using Bootstrap Methods: Accepted December 2025

Sami Akdeniz; Tuğba Özkal Yıldız

Forthcoming

On the Performance of Mean Estimators Under Ranked Set Sampling Techniques Using Bootstrap Methods

Accepted December 2025

Authors

Sami Akdeniz Dokuz Eylul University https://orcid.org/0000-0002-5515-079X
Tuğba Özkal Yıldız Dokuz Eylul University https://orcid.org/0000-0002-8552-2806

Keywords:

ranked set sampling, mean estimators, bootstrap, confidence interval, cardiovascular disease dataset

Abstract

Ranked Set Sampling (RSS) offers an alternative to Simple Random Sampling (SRS) when ranking units is easy but obtaining precise measurements is costly or time-consuming. To minimize ranking errors and improve parameter estimation, several modified methods of RSS have been proposed. Since the exact distribution of statistic is often unknown, especially with small samples, resampling methods such as the bootstrap are widely applied. This study examines bootstrap methods under RSS and compares the performance of mean estimators based on SRS, RSS and its modified methods through simulations, focusing on coverage probabilities and confidence interval widths using real data.

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FP_REVSTAT2025_dez3

Published

2025-12-30

Issue

new article

Section

Forthcoming Paper

License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Akdeniz, S., & Özkal Yıldız, T. (2025). On the Performance of Mean Estimators Under Ranked Set Sampling Techniques Using Bootstrap Methods: Accepted December 2025. REVSTAT-Statistical Journal. https://revstat.ine.pt/index.php/REVSTAT/article/view/1104

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