Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme
In recent years, joint censoring schemes have gained significant attention in lifetime experiments and reliability analysis. A refined approach, known as the balanced joint progressive censoring scheme, has been introduced in statistical studies. This research focuses on statistical inference for tw...
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/9/1536 |
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| author | Yuanqi Wang Jinchen Xiang Wenhao Gui |
| author_facet | Yuanqi Wang Jinchen Xiang Wenhao Gui |
| author_sort | Yuanqi Wang |
| collection | DOAJ |
| description | In recent years, joint censoring schemes have gained significant attention in lifetime experiments and reliability analysis. A refined approach, known as the balanced joint progressive censoring scheme, has been introduced in statistical studies. This research focuses on statistical inference for two Lomax populations under this censoring framework. Maximum likelihood estimation is employed to derive parameter estimates, and asymptotic confidence intervals are constructed using the observed Fisher information matrix. From a Bayesian standpoint, posterior estimates of the unknown parameters are obtained under informative prior assumptions. To evaluate the effectiveness and precision of these estimators, a numerical study is conducted. Additionally, a real dataset is analyzed to demonstrate the practical application of these estimation methods. |
| format | Article |
| id | doaj-art-3916c49f9e014f0cbff80d6a8cf2515d |
| institution | DOAJ |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-3916c49f9e014f0cbff80d6a8cf2515d2025-08-20T02:58:47ZengMDPI AGMathematics2227-73902025-05-01139153610.3390/math13091536Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring SchemeYuanqi Wang0Jinchen Xiang1Wenhao Gui2School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, ChinaSchool of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, ChinaIn recent years, joint censoring schemes have gained significant attention in lifetime experiments and reliability analysis. A refined approach, known as the balanced joint progressive censoring scheme, has been introduced in statistical studies. This research focuses on statistical inference for two Lomax populations under this censoring framework. Maximum likelihood estimation is employed to derive parameter estimates, and asymptotic confidence intervals are constructed using the observed Fisher information matrix. From a Bayesian standpoint, posterior estimates of the unknown parameters are obtained under informative prior assumptions. To evaluate the effectiveness and precision of these estimators, a numerical study is conducted. Additionally, a real dataset is analyzed to demonstrate the practical application of these estimation methods.https://www.mdpi.com/2227-7390/13/9/1536balanced joint progressive censoringLomax distributionmaximum likelihood estimationBayesian estimationMetropolis–Hastings algorithm |
| spellingShingle | Yuanqi Wang Jinchen Xiang Wenhao Gui Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme Mathematics balanced joint progressive censoring Lomax distribution maximum likelihood estimation Bayesian estimation Metropolis–Hastings algorithm |
| title | Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme |
| title_full | Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme |
| title_fullStr | Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme |
| title_full_unstemmed | Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme |
| title_short | Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme |
| title_sort | statistical inference for two lomax populations under balanced joint progressive type ii censoring scheme |
| topic | balanced joint progressive censoring Lomax distribution maximum likelihood estimation Bayesian estimation Metropolis–Hastings algorithm |
| url | https://www.mdpi.com/2227-7390/13/9/1536 |
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