A Hazard‐Based Regression Model Under the Exponentiated Alpha Power Log‐Logistic Distribution With Survival Data.
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| Title: | A Hazard‐Based Regression Model Under the Exponentiated Alpha Power Log‐Logistic Distribution With Survival Data. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 1687952X |
| DOI: | 10.1155/jpas/5953864 |
