A Hazard‐Based Regression Model Under the Exponentiated Alpha Power Log‐Logistic Distribution With Survival Data
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.
Fields of Study & Subjects
Statistical Distribution Estimation and Applications
Statistical Methods and Bayesian Inference
Statistical Methods and Inference
Publication details
- Journal / source
- Journal of Probability and Statistics
- Publisher
- Wiley
- Volume
- 2026
- Issue
- 1
- Publication date
- 1 January 2026
- Language
- EN