LEVERAGING RANK SET SAMPLING FOR ENHANCED STRESS-STRENGTH ESTIMATION IN THE CONTEXT OF NAKAGAMI DISTRIBUTION

This study addresses the estimation of the stress-strength reliability model, where stress and strength both following the Nakagami distribution. While conventional approaches have relied on simple random sampling (SRS) for estimating reliability models, recent research suggests that ranked set sampling (RSS) offers a more efficient alternative. RSS yields more informative samples compared to SRS, potentially enhancing the accuracy of reliability estimations. Our investigation focuses on deriving maximum likelihood estimators (MLEs) for stress-strength under both SRS and RSS methodologies. To evaluate the comparative efficacy of these sampling techniques, we conduct a comprehensive Monte Carlo simulation study. The results of this analysis provide compelling evidence that RSS-based estimators outperform their SRS counterparts in terms of efficiency and precision. This research contributes to the growing body of literature supporting the adoption of RSS in reliability engineering. By demonstrating the superior performance of RSS in the context of Nakagami-distributed stress-strength models, we offer valuable insights for researchers and practitioners seeking to optimize their estimation procedures in reliability analysis.