Estimation of multicomponent stress-strength reliability from exponentiated inverse Rayleigh distribution
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Taylor & Francis
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Abstract. Full text available at https://doi.org/10.1080/09720510.2020.1761094
This paper deals with the classical and Bayesian estimation of multicomponent stress-strength reliability through assuming the exponentiated inverse Rayleigh distribution. Assuming that both stress and strength variates are follows to exponentiated inverse Rayleigh distribution with common and known shape parameter. The multicomponent stress-strength reliability of a system is obtained by the methods of maximum likelihood and Bayesian approach. The results are compared using Markov Chain Monte Carlo (MCMC) technique for both small and large samples. Finally, two data sets of coating weights of iron sheets are analyzed for illustrative purposes.
This paper deals with the classical and Bayesian estimation of multicomponent stress-strength reliability through assuming the exponentiated inverse Rayleigh distribution. Assuming that both stress and strength variates are follows to exponentiated inverse Rayleigh distribution with common and known shape parameter. The multicomponent stress-strength reliability of a system is obtained by the methods of maximum likelihood and Bayesian approach. The results are compared using Markov Chain Monte Carlo (MCMC) technique for both small and large samples. Finally, two data sets of coating weights of iron sheets are analyzed for illustrative purposes.
Keywords
Bayesian estimation, Maximum likelihood estimation, ML estimation, Rayleigh distribution, Multicomponent stress-strength reliability, Markov Chain Monte Carlo, MCMC, Exponentiated inverse rayleigh distribution, Bayesian, Parameter