Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data
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 compre...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-03-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/12/6/874 |
| _version_ | 1797240172065062912 |
|---|---|
| author | Kalanka P. Jayalath |
| author_facet | Kalanka P. Jayalath |
| author_sort | Kalanka P. Jayalath |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T18:03:12Z |
| format | Article |
| id | doaj.art-ef34ed1fc84747ec819ee2014b377e77 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-04-24T18:03:12Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-ef34ed1fc84747ec819ee2014b377e772024-03-27T13:53:10ZengMDPI AGMathematics2227-73902024-03-0112687410.3390/math12060874Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders DataKalanka P. Jayalath0Department of Mathematics and Statistics, University of Houston—Clear Lake, Houston, TX 77058, USAThis 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.https://www.mdpi.com/2227-7390/12/6/874Bayesian inferencecensoringGibbs samplerJeffrey’s Priorreference prior |
| spellingShingle | Kalanka P. Jayalath Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data Mathematics Bayesian inference censoring Gibbs sampler Jeffrey’s Prior reference prior |
| title | Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data |
| title_full | Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data |
| title_fullStr | Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data |
| title_full_unstemmed | Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data |
| title_short | Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data |
| title_sort | improved bayesian inferences for right censored birnbaum saunders data |
| topic | Bayesian inference censoring Gibbs sampler Jeffrey’s Prior reference prior |
| url | https://www.mdpi.com/2227-7390/12/6/874 |
| work_keys_str_mv | AT kalankapjayalath improvedbayesianinferencesforrightcensoredbirnbaumsaundersdata |