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首页> 外文期刊>Journal of applied statistics >Bayes estimation for exponential distributions with common location parameter and applications to multi-state reliability models
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Bayes estimation for exponential distributions with common location parameter and applications to multi-state reliability models

机译：具有公共位置参数的指数分布的贝叶斯估计及其在多状态可靠性模型中的应用

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This paper considers the estimation of the stress-strength reliability of a multi-state component or of a multi-state system where its states depend on the ratio of the strength and stress variables through a kernel function. The article presents a Bayesian approach assuming the stress and strength as exponentially distributed with a common location parameter but different scale parameters. We show that the limits of the Bayes estimators of both location and scale parameters under suitable priors are the maximum likelihood estimators as given by Ghosh and Razmpour [15]. We use the Bayes estimators to determine the multi-state stress-strength reliability of a system having states between 0 and 1. We derive the uniformly minimum variance unbiased estimators of the reliability function. Interval estimation using the bootstrap method is also considered. Under the squared error loss function and linex loss function, risk comparison of the reliability estimators is carried out using extensive simulations.
机译：本文考虑了多状态组件或多状态系统的应力-强度可靠性的估计，其状态取决于通过核函数的强度和应力变量之比。本文提出了一种贝叶斯方法，该方法假设应力和强度以共同的位置参数但具有不同的比例参数呈指数分布。我们表明，在适当的先验条件下，位置和比例参数的贝叶斯估计量的极限是由Ghosh和Razmpour [15]给出的最大似然估计量。我们使用贝叶斯估计器来确定状态介于0和1之间的系统的多状态应力-强度可靠性。我们得出了可靠性函数的一致最小方差无偏估计器。还考虑了使用自举方法的间隔估计。在平方误差损失函数和线损损失函数下，使用大量模拟对可靠性估计量进行风险比较。

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