Statistical inference of reliability in multicomponent stress strength model for pareto distribution based on upper record values

Azhad, Qazi J.; Arshad, Mohd Anuar; Khandelwal, Nancy

doi:10.1080/02286203.2021.1891496

Cited by 15 publications

(6 citation statements)

References 55 publications

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“…So it is a practical direction to extend the independent components in this paper to a multi-component system. In this regard, Rao et al 25,26 and Azhad et al 24 are recommended.…”

Section: Discussionmentioning

confidence: 99%

“…More details about Bayesian computation methods can refer to Albert. 22 For more research on Bayes method, Arshad et al, 23 Bai et al, 10 and Azhad et al 24 are recommended.…”

Section: Bayesian Estimationmentioning

confidence: 99%

“…More details about Bayesian computation methods can refer to Albert. 22 For more research on Bayes method, Arshad et al, 23 Bai et al, 10 and Azhad et al 24 are recommended. When selecting the prior distribution of parameters, the conjugate prior distribution is often the first choice.…”

Section: Bayesian Estimationmentioning

confidence: 99%

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Inference for cumulative risk model under step-stress experiments and its application in nanocrystalline data

Qiao

Gui

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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With the popularity of step-stress accelerated life testing, researchers are exploring more possibilities for models that relate the life distributions under different stress levels. Cumulative risk model assumes that the effects of stress changes have a lag period before they are fully observed, which guarantees the continuity of the hazard rate function. This paper studies the cumulative risk model for Lomax distribution with step-stress experiments. For maximum likelihood estimation, Newton-Rapson method is adopted to get point estimates. Meanwhile, the asymptotic normality of the maximum likelihood estimator is used to obtain asymptotic confidence intervals. For Bayesian estimation, point estimates and highest posterior density credible intervals under squared error loss function with informative prior and non-informative prior are derived using Metropolis-Hastings method and Metropolis-Hastings within Gibbs algorithm. To evaluate the effects of stress change time and the length of lag period, as well as the performance of different methods, numerical simulations are conducted. Then a real nanocrystalline data set is analyzed.

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“…So it is a practical direction to extend the independent components in this paper to a multi-component system. In this regard, Rao et al 25,26 and Azhad et al 24 are recommended.…”

Section: Discussionmentioning

confidence: 99%

“…More details about Bayesian computation methods can refer to Albert. 22 For more research on Bayes method, Arshad et al, 23 Bai et al, 10 and Azhad et al 24 are recommended.…”

Section: Bayesian Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Inference for cumulative risk model under step-stress experiments and its application in nanocrystalline data

Qiao

Gui

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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“…In this regard, we generated random samples from stress and strength variables for different combinations of parameters and sample sizes 10, 30 and 50. We estimated the reliability of the MSS model in two cases (s, k) = (1, 4) and (2,5) .The criteria of mean square error (MSE), bias, average length (AL) as well as coverage probability (CP) at confidence level of 95%, were used to evaluate the simulation results.…”

Section: Simulation Studymentioning

confidence: 99%

“…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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Advancements in reliability estimation for the exponentiated Pareto distribution: a comparison of classical and Bayesian methods with lower record values

Saini

2024

Comput Stat

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Statistical inference of reliability in multicomponent stress strength model for pareto distribution based on upper record values

Cited by 15 publications

References 55 publications

Inference for cumulative risk model under step-stress experiments and its application in nanocrystalline data

Inference for cumulative risk model under step-stress experiments and its application in nanocrystalline data

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

Advancements in reliability estimation for the exponentiated Pareto distribution: a comparison of classical and Bayesian methods with lower record values

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