Statistical analysis of adaptive progressive type-II censored competing risks data from generalized logistic distribution with application

This paper provides a detailed statistical analysis of adaptive progressive type-II censored competing risks data, modeled with the generalized logistic distribution. Building on previous research that explored maximum likelihood estimation and Bayesian inference under various censoring schemes, this study emphasizes the generalized logistic distribution’s adaptability for skewed data, making it highly applicable to reliability and life-testing studies. This paper outlines the process of deriving maximum likelihood estimates for the unknown parameters and discusses the calculation of Bayes estimates and their credible intervals using the Markov chain Monte Carlo approach. A Monte Carlo simulation study assesses the effectiveness of these proposed methods, demonstrating their practical utility. The methodologies are further validated using real-world datasets, underscoring their relevance in statistical modeling and inference. Overall, this research contributes to the literature by deepening the understanding of adaptive progressive type-II censoring in competing risks scenarios, particularly within the context of the generalized logistic distribution.