A Hazard‐Based Regression Model Under the Exponentiated Alpha Power Log‐Logistic Distribution With Survival Data.
Saved in:
| Title: | A Hazard‐Based Regression Model Under the Exponentiated Alpha Power Log‐Logistic Distribution With Survival Data. |
|---|---|
| Authors: | Kariuki, Veronica1 (AUTHOR) vewkariuki@usiu.ac.ke, Barbiero, Alessandro1 (AUTHOR) alessandro.barbiero@unimi.it |
| Source: | Journal of Probability & Statistics. 5/18/2026, Vol. 2026, p1-12. 12p. |
| Subjects: | Hazard function (Statistics), Survival analysis (Biometry), Distribution (Probability theory), Monte Carlo method, Maximum likelihood statistics |
| Abstract: | This study introduces a hazard‐based regression model incorporating the exponentiated alpha‐power log‐logistic (EAPLL) baseline distribution. Specifically, the proposed model follows the EAPLL–accelerated failure time (AFT) framework, and we establish that the EAPLL distribution remains closed under the AFT model. The model parameters are estimated using the maximum likelihood estimation method. A Monte Carlo simulation is conducted to assess the performance of the estimators across various scenarios based on an increasing baseline hazard function shape. Finally, the applicability of the proposed model is demonstrated using real‐life censored survival data. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Probability & Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 193836851 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A Hazard‐Based Regression Model Under the Exponentiated Alpha Power Log‐Logistic Distribution With Survival Data. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kariuki%2C+Veronica%22">Kariuki, Veronica</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vewkariuki@usiu.ac.ke</i><br /><searchLink fieldCode="AR" term="%22Barbiero%2C+Alessandro%22">Barbiero, Alessandro</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> alessandro.barbiero@unimi.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Probability+%26+Statistics%22">Journal of Probability & Statistics</searchLink>. 5/18/2026, Vol. 2026, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hazard+function+%28Statistics%29%22">Hazard function (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Survival+analysis+%28Biometry%29%22">Survival analysis (Biometry)</searchLink><br /><searchLink fieldCode="DE" term="%22Distribution+%28Probability+theory%29%22">Distribution (Probability theory)</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study introduces a hazard‐based regression model incorporating the exponentiated alpha‐power log‐logistic (EAPLL) baseline distribution. Specifically, the proposed model follows the EAPLL–accelerated failure time (AFT) framework, and we establish that the EAPLL distribution remains closed under the AFT model. The model parameters are estimated using the maximum likelihood estimation method. A Monte Carlo simulation is conducted to assess the performance of the estimators across various scenarios based on an increasing baseline hazard function shape. Finally, the applicability of the proposed model is demonstrated using real‐life censored survival data. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Probability & Statistics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=193836851 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/jpas/5953864 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Hazard function (Statistics) Type: general – SubjectFull: Survival analysis (Biometry) Type: general – SubjectFull: Distribution (Probability theory) Type: general – SubjectFull: Monte Carlo method Type: general – SubjectFull: Maximum likelihood statistics Type: general Titles: – TitleFull: A Hazard‐Based Regression Model Under the Exponentiated Alpha Power Log‐Logistic Distribution With Survival Data. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kariuki, Veronica – PersonEntity: Name: NameFull: Barbiero, Alessandro IsPartOfRelationships: – BibEntity: Dates: – D: 18 M: 05 Text: 5/18/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1687952X Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Probability & Statistics Type: main |
| ResultId | 1 |
