Estimation of the Stress-Strength Reliability for the Dagum Distribution

Alzahrani, Bander A.; Basloom, Samerah

doi:10.22606/jas.2016.13005

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…So, it can be selected as the suitable model. Now, we obtain the reliability of the MSS model through classical and Bayesian methods for (s, k)=(1,3), (2,4), (1,4) and (2,5). First, from the above data sets, the ML estimates of α and β were computed as α = 0.6570 and β = 1.1886, respectively.…”

Section: Real Examplementioning

confidence: 99%

Estimation of Multicomponent Stress-strength Reliability under Inverse Topp-Leone Distribution

Pasha-Zanoosi¹

2022

Pak.j.stat.oper.res.

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In this article, the reliability inference for a multicomponent stress-strength (MSS) model, when both stress and strength random variables follow inverse Topp-Leone distributions, was studied. The maximum likelihood and uniformly minimum variance unbiased estimates for the reliability of MSS model were obtained explicitly. The exact Bayes estimate of MSS reliability was derived the under squared error loss function. Also, the Bayes estimate was obtained using the Monte Carlo Markov Chain method for comparison with the aforementioned exact estimate. The asymptotic confidence interval was determined under the expected Fisher information matrix. Furthermore, the highest probability density credible interval was established through using Gibbs sampling method. Monte Carlo simulations were implemented to compare the different proposed methods. Finally, a real life example was presented in support of the suggested procedures.

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Section: Real Examplementioning

confidence: 99%

Estimation of Multicomponent Stress-strength Reliability under Inverse Topp-Leone Distribution

Pasha-Zanoosi¹

2022

Pak.j.stat.oper.res.

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

PASHA-ZANOOSİ

Pourdarvish

2022

Hacettepe Journal of Mathematics and Statistics

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This article deals with the problem of reliability in a multicomponent stress-strength (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.

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Estimation of the Stress-Strength Reliability for the Dagum Distribution

Cited by 2 publications

References 14 publications

Estimation of Multicomponent Stress-strength Reliability under Inverse Topp-Leone Distribution

Estimation of Multicomponent Stress-strength Reliability under Inverse Topp-Leone Distribution

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

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