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- Title
Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data.
- Authors
Jayalath, Kalanka P.
- Abstract
This work focuses on making Bayesian inferences for the two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. A flexible Gibbs sampler is employed to handle the censored BS data in this Bayesian work that relies on Jeffrey's and Achcar's reference priors. A comprehensive simulation study is conducted to compare estimates under various parameter settings, sample sizes, and levels of censoring. Further comparisons are drawn with real-world examples involving Type-II, progressively Type-II, and randomly right-censored data. The study concludes that the suggested Gibbs sampler enhances the accuracy of Bayesian inferences, and both the amount of censoring and the sample size are identified as influential factors in such analyses.
- Subjects
BAYESIAN field theory; GIBBS sampling; CENSORING (Statistics); FACTOR analysis; SAMPLE size (Statistics)
- Publication
Mathematics (2227-7390), 2024, Vol 12, Issue 6, p874
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math12060874