Bayesian inference for the parameters of the generalized logistic distribution under a combined framework of generalized type-I and type-II hybrid censoring schemes with application to physical data

This study focuses on the Bayesian inference of parameters for the generalized logistic distribution, utilizing a combined framework of generalized type-I and type-II hybrid censoring schemes. The research addresses limitations in existing censoring methods by proposing a flexible model that enhances practical applicability in reliability and life-testing studies. Key objectives include the development of maximum likelihood estimators and asymptotic confidence intervals, alongside Bayesian estimation techniques using Markov chain Monte Carlo methods. These advancements facilitate the computation of credible intervals under various loss functions, thereby improving estimation efficiency. The paper also includes a comprehensive analysis of real-world datasets and simulation experiments to validate the proposed methodologies. A comparative evaluation of different estimators highlights the superiority of the combined framework of generalized type-I and type-II hybrid censoring schemes, providing valuable insights into the reliability and performance of the estimators. Overall, this research contributes significantly to the understanding and application of the generalized logistic distribution, offering practical tools for researchers and practitioners in the field of reliability engineering.