Multicomponent stress-strength reliability based on a right long-tailed distribution

Authors : Hossein PASHAZANOOSİ, Ahmad POURDARVİSH

Pages : 559-586

Doi:10.15672/hujms.880993

View : 47 | Download : 13

Publication Date : 2022-04-01

Article Type : Research Paper

Abstract :This article deals with the problem of reliability in a multicomponent stress-strength insert ignore into journalissuearticles values(MSS); model when both stress and strength variables are from inverse Kumaraswamy distribution. The reliability of the system is estimated using classical and Bayesian approaches when the common second shape parameter is known or unknown. The maximum likelihood estimation and its asymptotic confidence interval for the reliability of the system are obtained. Furthermore, two other asymptotic confidence intervals are computed based on Logit and Arcsin transformations. The uniformly minimum variance unbiased estimator for the reliability of the MSS model is obtained when the common second shape parameter is known. The Bayes estimate is obtained exactly when the second shape parameter is known and it is approximated by using the Monte Carlo Markov Chain method when the second shape parameter is unknown. The highest probability density credible interval is established using the Gibbs sampling technique. Monte Carlo simulations are implemented to compare the different proposed methods. Finally, two real data sets are presented in support of the suggested procedures.
Keywords : Inverse Kumaraswamy distribution, Multicomponent stress strength model, Right long tailed distribution

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