In this paper, we investigate parameter inference for the Kumaraswamy distribution based on progressively type-II censored data. Our approach involves employing the method of maximum likelihood to derive point estimates for the model parameters. We establish the existence and uniqueness of these maximum likelihood estimators. Additionally, we present pivotal quantities that enable the construction of exact confidence intervals and joint confidence regions for the model parameters. To assess the performance of our proposed estimation techniques, we conduct comprehensive simulation studies. In conclusion, we apply the introduced estimation methods to analyze and discuss the results obtained from three real datasets, providing practical insights into their performance. These exact joint confidence regions can be directly utilized to construct confidence bounds for various reliability indices and quality control measures, enhancing their applicability in industrial settings.
