TY  - EJOU
AU  - Almetwally, Ehab M. 
AU  - Khaled, O. M. 
AU  - Barakat, H. M. 

TI  - On Progressive-Stress ALT under Generalized Progressive Hybrid Censoring Scheme for Quasi Xgamma Distribution
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 143
IS  - 3
SN  - 1526-1506

AB  - Accelerated life tests play a vital role in reliability analysis, especially as advanced technologies lead to the production of highly reliable products to meet market demands and competition. Among these tests, progressive-stress accelerated life tests (PSALT) allow for continuous changes in applied stress. Additionally, the generalized progressive hybrid censoring (GPHC) scheme has attracted significant attention in reliability and survival analysis, particularly for handling censored data in accelerated testing. It has been applied to various failure models, including competing risks and step-stress models. However, despite its growing relevance, a notable gap remains in the literature regarding the application of GPHC in PSALT models. This paper addresses that gap by studying PSALT under a GPHC scheme with binomial removal. Specifically, it considers lifetimes following the quasi-Xgamma distribution. Model parameters are estimated using both maximum likelihood and Bayesian methods under gamma priors. Interval estimation is provided through approximate confidence intervals, bootstrap methods, and Bayesian credible intervals. Bayesian estimators are derived under squared error and entropy loss functions, using informative priors in simulation and non-informative priors in real data applications. A simulation study is conducted to evaluate various censoring schemes, with coverage probabilities and interval widths assessed via Monte Carlo simulations. Additionally, Bayesian predictive estimates and intervals are presented. The proposed methodology is illustrated through the analysis of two real-world accelerated life test datasets.
KW  - Progressive-stress; progressive hybrid censoring; maximum likelihood estimation; Bayes estimation; simulation study

DO  - 10.32604/cmes.2025.065446