Reliability assessment of heterogeneous Dagum-distributed multi-component stress-strength systems under adaptive hybrid progressive censoring - Akram Kohansal (20545673) (2025)
This paper develops comprehensive inference procedures for multi-component stress-strength reliability models with heterogeneous Dagum-distributed component strengths under adaptive hybrid progressive censoring (AHPC). We propose both classical and Bayesian estimation strategies, including maximum likelihood estimators (MLEs), asymptotic confidence intervals, approximate Bayesian estimators, and highest posterior density (HPD) credible intervals. A Markov chain Monte Carlo (MCMC) framework combined with Gibbs sampling is employed to handle complex posterior distributions efficiently. The methodology is validated through simulation studies and real-world data analysis, demonstrating the model’s flexibility in capturing heavy-tailed lifetime behavior and providing actionable insights for reliability planning, component selection, and preventive maintenance scheduling. The results highlight that incorporating AHPC schemes leads to improved estimation accuracy and practical adaptability in reliability engineering. Other Information Published in: Journal of Computational and Applied Mathematics License: http://creativecommons.org/licenses/by/4.0/ See article on publisher's website: https://dx.doi.org/10.1016/j.cam.2025.117238