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CONFIDENCE INTERVAL FOR RELIABILITY $R = P (X
Keywords:
Rayleigh distribution, confidence interval, reliabilityDOI:
https://doi.org/10.17654/0972361724045Abstract
This paper proposes a novel method for evaluating the reliability of stress strength models, in particular when two independent Rayleigh random variables are involved. It introduces likelihood-based procedures to construct confidence intervals for reliability. Empirical verification of the estimators is systematically conducted across a range of diverse scenarios. This research has significant implications for fields such as engineering and reliability analysis, providing valuable insights into how well things hold up in diverse conditions.
Received: January 30, 2024
Accepted: April 25, 2024
References
L. Rayleigh, On the stability, or instability, of certain fluid motions, Proc. London Math. Soc. 9 (1880), 57-70.
A. Barbiero, Confidence intervals for reliability of stress-strength models in the normal case, Comm. Statist. Simulation Comput. 40(6) (2011), 907-925.
M. Kilai, G. A. Waititu, W. A. Kibira, M. M. Abd El-Raouf and T. A. Abushal, A new versatile modification of the Rayleigh distribution for modeling COVID-19 mortality rates, Results in Physics 35 (2022), 105260.
R. Alizadeh Noughabi, H. Alizadeh Noughabi and A. Ebrahimi Moghaddam Behabadi, An entropy test for the Rayleigh distribution and power comparison, J. Stat. Comput. Simul. 84(1) (2014), 151-158.
K. Ateeq, T. B. Qasim and A. R. Alvi, An extension of Rayleigh distribution and applications, Cogent Mathematics and Statistics 6(1) (2019), 1622191.
N. Balakrishnan, Approximate MLE of the scale parameter of the Rayleigh distribution with censoring, IEEE Transactions on Reliability 38(3) (1989), 355 357.
S. J. Wu, D. H. Chen and S. T. Chen, Bayesian inference for Rayleigh distribution under progressive censored sample, Applied Stochastic Models in Business and Industry 22(3) (2006), 269-279.
Jiexiang Li, Estimation of parameters of Rayleigh distributions, Advances and Applications in Statistics 27(1) (2012), 1-8.
E. E. Afify, Comparison of estimators of parameters for the Rayleigh distribution, 2003. Online text
http://jscs.stat.vt.edu/interstat/articles/2003/abstracts/u03001.htmlssi.
A. A. Al-Babtain, A new extended Rayleigh distribution, Journal of King Saud University-Science 32(5) (2020), 2576-2581.
M. M. Smadi and M. H. Alrefaei, New extensions of Rayleigh distribution based on inverted-Weibull and Weibull distributions, International Journal of Electrical and Computer Engineering 11(6) (2021), 5107-5118.
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