multicomponent stress-strength parameter in exponentiated kumaraswamy distribution and progressive censoring

This study conducts statistical inference on a multicomponent reliability stress-strength system with non-identical component strengths, using the exponentiated kumaraswamy distribution for progressively censored samples. we investigate estimation of the system reliability parameter via both classical and bayesian inference frameworks, including maximum likelihood estimation, approximate bayesian estimation, and highest posterior density interval construction. we compare the performance of different estimators using monte carlo simulation, with evaluation metrics including mean squared error and coverage probability. a real-data example is also provided to demonstrate the effectiveness of the proposed model, highlighting its practical utility and relevance for both reliability engineering and statistical analysis.