Reliability inference for dependent multicomponent stress-strength model with bounded strength based on copula

In this article, we explore the reliability inference for a dependent multicomponent stress-strength model with bounded strength in a series system. The Joe copula is used to characterize the dependence structure between stresses and strength variables, which follow the proportional hazard rates model. We establish maximum likelihood estimators for model parameters and system reliability, along with improved approximate confidence intervals based on the Fisher information matrix. Bayesian estimators are performed using the flexible Gamma-Dirichlet prior under symmetric and asymmetric loss functions, and the highest posterior density credible intervals are obtained via the Metropolis-Hastings algorithm. Monte Carlo simulations are conducted to illustrate the performance of the proposed methods. Finally, a real dataset, the breaking strength of jute fiber, is examined for illustrative purposes.