This study deals with the classical and Bayesian estimation of reliability in a multicomponent stress–strength model by assuming that both stress and strength variables follow exponentiated Pareto distribution. First, the maximum likelihood method is used to estimate reliability. The asymptotic confidence interval is constructed. We also propose two bootstrap confidence intervals. Next, the Bayesian estimates of reliability are obtained using Lindley’s approximation, Tierney–Kadane approximation and the Markov chain Monte Carlo (MCMC) method since there are no explicit forms. The MCMC method is used to construct the Bayesian credible interval. A Monte Carlo simulation study is performed to compare the performance of the corresponding methods. Finally, the hydrological data set is analyzed in the application part.

本研究通过假设应力和强度变量都遵循指数帕累托分布来处理多分量应力-强度模型中可靠性的经典和贝叶斯估计。首先，最大似然法用于估计可靠性。构建渐近置信区间。我们还提出了两个 bootstrap 置信区间。接下来，由于没有明确的形式，因此使用 Lindley 近似、Tierney-Kadane 近似和马尔可夫链蒙特卡罗 (MCMC) 方法获得可靠性的贝叶斯估计。MCMC 方法用于构造贝叶斯可信区间。进行蒙特卡罗模拟研究以比较相应方法的性能。最后，在应用部分对水文数据集进行了分析。