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Statistical inference for two Burr-XII populations under balanced joint progressive censoring with competing risks
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DOI：10.1080/00949655.2026.2654042.png)
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Abstract
En 中文
{Simulation studies and an illustrative real-data analysis are used to validate a competing-risks inference framework under balanced joint progressive censoring, demonstrating significant improvements in reliability analysis. } Comparative lifetime testing is essential for research assessing the relative performance of two identical products produced on separate manufacturing lines. This article proposes a statistical inference framework for analyzing competing risks data under balanced joint progressive censoring using the Burr-XII distribution, providing a systematic approach for the analysis of competing risks in comparative lifetime studies. We develop the maximum likelihood estimation procedures for the unknown parameters, rigorously establishing the existence and uniqueness of the resulting estimators. Additionally, we construct four types of confidence intervals: approximate confidence intervals (ACIs), lognormal ACIs, as well as bootstrap-p and bootstrap-t confidence intervals. For Bayesian inference, we present the parameter estimation under three distinct loss functions and derive the highest posterior density credible intervals through Markov Chain Monte Carlo simulations. The performance of all methods is comprehensively evaluated through simulation studies and real data analysis.
Keywords:
Balanced joint progressive censoring
competing risks model
Burr-XII distribution
maximum likelihood estimation
Bayesian inference
Markov Chain Monte Carlo
Journal
J
IF:
1.2
Papers:
114
Citations:
4.1K
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