Reliability Estimation for the Remained Stress-Strength Model under the Generalized Exponential Lifetime Distribution

Saber, Mohammad Mehdi; El-Din, M. M. Mohie; Yousof, Haitham M.

doi:10.1155/2021/7363449

Cited by 12 publications

(9 citation statements)

References 10 publications

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“…(Refer Saber and Khorshidian(2021) ). Motivated by this, Saber and Khorshidian [10] introduced the conditional stress -strength reliability R |a,b . Later researchers such as Raid et al [11], and Sabre et al [12] have attempted to estimate reliability under a conditional stress-strength setup in recent years.…”

Section: Introductionmentioning

confidence: 99%

“…Motivated by this, Saber and Khorshidian [10] introduced the conditional stress -strength reliability R |a,b . Later researchers such as Raid et al [11], and Sabre et al [12] have attempted to estimate reliability under a conditional stress-strength setup in recent years. They evaluated the system's reliability in a conditional stress-strength setup where the stress-strength variable follows Kumaraswamy, and Generalised exponential lifetime distributions.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Reliability under Conditional Stress - Strength Setup based on Weibull Distribution

Architha M,

Pandit

2024

AJPAS

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The Weibull distribution has been extensively studied and applied across various fields due to its versatility in modeling a wide range of phenomena, especially in reliability engineering, survival analysis, and lifetime modeling. The concept of Rl a,b , which represents a system's reliability in a conditional stress-strength setup, was proposed by Sabre and Khorshidian (2021). In this research, the problem of estimating reliability of the component is considered when strength variable X and stress variable Y follow independent Weibull distributions with common shapes and different scale parameters under conditional stress-strength setup. The maximum likelihood estimator, asymptotic confidence interval, Bootstrap estimators, Boot-p estimators, and Bayes estimator under-squared error loss function with associated highest posterior density interval are constructed for conditional stress-strength reliability. Simulation study is conducted to estimate mean square error (MSE) of estimator of conditional stress-strength reliability. The real data analysis is also carried out.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Reliability under Conditional Stress - Strength Setup based on Weibull Distribution

Architha M,

Pandit

2024

AJPAS

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“…However, our discussion in this article will center around the practical applications and implications of the Poisson Topp-Leone Burr XII (PTL-BXII) model. For more Burr XII extensions see Paranaíba et al [43], Paranaíba et al [42], Yousof et al [58], Korkmaz et al [32], Yousof et al [61], Gad et al [25], Cordeiro [19] and Elsayed and Yousof [24]. Some other useful Burr XII extensions can found in Nasir et al [39], Yousof et al [61], Nasir et al [40], Abouelmagd et al [2] and Abouelmagd et al [3].…”

Section: Introductionmentioning

confidence: 99%

Modified Bagdonavicius-Nikulin Goodness-of-fit Test Statistic for the Compound Topp Leone Burr XII Model with Various Censored Applications

Khalil,

Aidi,

Ali

et al. 2024

Stat., optim. inf. comput.

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The Poisson Topp Leone Burr XII distribution is extensively studied due to its broad relevance in analyzing censored real datasets from engineering, economics, and medicine. In this research, the distribution's versatility is highlighted through the analysis of four specific real datasets. The study compares the Poisson Topp Leone Burr XII distribution with nine extensions of the Burr type XII distribution to determine which offers the best fit for these datasets. To evaluate the goodness-of-fit of the Poisson Topp Leone Burr XII distribution under right censoring, a modified Bagdonavi\v{c}ius-Nikulin goodness-of-fit test statistic is introduced and applied. This new test statistic is utilized to validate the distributional fit for the Poisson Topp Leone Burr XII distribution across the four right-censored datasets. The modified Bagdonavi\v{c}ius-Nikulin test statistic is employed to assess distributional validation, specifically in the context of right censoring. The application of this statistic involves analyzing each of the four censored datasets to confirm the appropriateness of the Poisson Topp Leone Burr XII distribution for these scenarios. Additionally, to support the evaluation of the modified goodness-of-fit test statistic, the Barzilai-Borwein algorithm is utilized. This algorithm is employed within a simulation study to further assess the effectiveness and reliability of the modified Bagdonavi\v{c}ius-Nikulin test statistic, thereby ensuring robust validation of the Poisson Topp Leone Burr XII distribution against the observed real datasets.

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“…A stress-strength reliability model compares the strength and stresses on a certain system; it is used primarily not only in reliability engineering and quality control but also in economics, psychology, and medicine [18]. In the area of stress-strength models, there has been a large amount of work regarding estimation of the reliability parameter, R � p(Y < X), where X and Y are independent random variables and have the same univariate distribution.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Khames and Mokhlis [19] introduced a bivariate general exponential model for the stress-strength reliability model. Saber et al [18] suggested a remained stress-strength model for the generalized exponential model. Jafari and Bafekri [20] made inferences using the stress-strength model for two parameters exponential distribution under the assumption of order statistics.…”

Section: Introductionmentioning

confidence: 99%

Stress-Strength Reliability and Randomly Censored Model of Two-Parameter Power Function Distribution

Liu

Khan

Anwar

et al. 2022

Mathematical Problems in Engineering

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The power function distribution is a flexible waiting time model that may provide better fit for some failure data. This paper presents the Bayes estimates of two-parameter power function distribution under progressive censoring. Different progressive censoring schemes have been used for the analysis. The Bayes estimates are obtained, using conjugate priors, under five loss functions including square error, precautionary, weighted, LINEX, and DeGroot loss function. The Gibbs sampling algorithm and Tierney and Kadane’s Approximation are used for the Bayes estimates of model parameters, reliability function, and stress-strength reliability. The comparison of the Bayes estimates is considered through the root mean squared errors. One real-life dataset is analyzed to illustrate the applications of proposed estimates. The results from the simulation study and real data analysis suggest that the Bayes estimation was more efficient for the progressive censoring schemes with all the withdrawals at the time of first failure.

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Reliability Estimation for the Remained Stress-Strength Model under the Generalized Exponential Lifetime Distribution

Cited by 12 publications

References 10 publications

Estimation of Reliability under Conditional Stress - Strength Setup based on Weibull Distribution

Estimation of Reliability under Conditional Stress - Strength Setup based on Weibull Distribution

Modified Bagdonavicius-Nikulin Goodness-of-fit Test Statistic for the Compound Topp Leone Burr XII Model with Various Censored Applications

Stress-Strength Reliability and Randomly Censored Model of Two-Parameter Power Function Distribution

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