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Bayesian reliability estimation of bivariate Marshal-Olkin exponential stress-strength model

机译:二元Marshal-Olkin指数应力强度模型的Bayes可靠性估计

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In this article we attempted reliability analysis of a component under the stress-strength pattern with both classical as well as Bayesian techniques. The main focus is made to develop the theory for dealing the reliability problems in various circumstances for bivariate environmental set up in context of Bayesian paradigm. A stress-strength based model describes the life of a component which has strength (Y) and is subjected to stress(X). We develop the Bayes and moment estimators of reliability of a component for each of the three possible conditions, under the assumption that the two stresses (i.e. X_1 and X_2) on a component are dependent and follow a Bivariate exponential (BE) of Marshall-Olkin distribution, the strength of a component (Y) following exponential distribution is independent of the stresses. The simulation study is performed with Markov Chain Monte Carlo technique via Gibbs sampler to obtain the estimates of Bayes estimators of reliability, are compared with moment estimators of reliabilities on the basis of absolute biases.
机译:在本文中,我们尝试使用经典技术和贝叶斯技术对应力强度模式下的组件进行可靠性分析。主要重点是发展在贝叶斯范式背景下建立双变量环境下处理各种情况下的可靠性问题的理论。基于应力强度的模型描述了具有强度(Y)并承受应力(X)的组件的寿命。我们在三个可能条件中的每一个条件下,针对组件的两个应力(即X_1和X_2)是依赖的,并遵循Marshall-Olkin的双变量指数(BE),开发了组件的贝叶斯和矩估计量分布(Y)遵循指数分布的强度与应力无关。通过Gibbs采样器使用Markov Chain Monte Carlo技术进行仿真研究,以获取可靠性的Bayes估计量的估计值,并在绝对偏差的基础上将其与可靠性的矩估计量进行比较。

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