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Classical and Bayesian reliability inference for the chen distribution under a block-adaptive progressive hybrid
Mahmoud M El-Awady1, Hanan Haj Ahmad2, Dina A Ramadan3
1Basic Sciences Department, Misr Higher Institute for Commerce and Computers, Mansoura, Egypt.
Plos One
|August 14, 2026
Summary
This study introduces efficient statistical inference for lifetime data using block-adaptive censoring, accelerating experiments. Bayesian methods provide more accurate reliability estimates and narrower intervals compared to classical approaches.
Area of Science:
- Statistics
- Reliability Engineering
- Survival Analysis
Background:
- Lifetime data analysis is crucial for product reliability and survival studies.
- Traditional censoring schemes can be time-consuming.
- Block-adaptive progressive hybrid censoring offers an efficient alternative.
Purpose of the Study:
- To develop statistical inference methods for lifetime data under block-adaptive progressive hybrid censoring.
- To compare classical and Bayesian estimation approaches for Chen distribution parameters and reliability measures.
- To assess the impact of inter-block heterogeneity on reliability estimations.
Main Methods:
- Utilizing the two-parameter Chen distribution for lifetime modeling.
- Applying classical and Bayesian statistical inference frameworks.
- Employing Markov chain Monte Carlo (MCMC) with Gibbs sampling and Metropolis-Hastings for Bayesian analysis.
- Conducting simulation studies to evaluate estimator performance (bias, MSE, interval width, coverage).
Main Results:
- Bayesian estimators demonstrated superior accuracy and narrower confidence intervals.
- The proposed censoring scheme effectively reduces experiment duration.
- Inter-block heterogeneity was analyzed for its effect on reliability.
Conclusions:
- The Bayesian approach, combined with block-adaptive censoring, offers an efficient and accurate method for lifetime data analysis.
- The methodology is practically applicable to fields like cancer survival and electrical breakdown analysis.
- This approach enhances reliability assessment while minimizing experimental time.
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