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

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.