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Research Article | Open Access

The accelerated failure time regression model under the extended-exponential distribution with survival analysis

Veronica Kariuki1( ), Anthony Wanjoya2, Oscar Ngesa3, Mahmoud M. Mansour4,5, Enayat M. Abd Elrazik4,5, Ahmed Z. Afify5
Department of Mathematics, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi 00200, Kenya
Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi 00200, Kenya
Department of Mathematics, Statistics and Physical Science, Taita Taveta University, Voi 80300, Kenya
Department of Management Information Systems, Yanbu, Taibah University, Yanbu 46422, Saudi Arabia
Department of Statistics, Mathematics, and Insurance, Benha University, Benha 13511, Egypt
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Abstract

In this paper, we propose a parametric accelerated failure time (AFT) hazard-based regression model with the extended alpha-power exponential (EAPE) baseline distribution. The proposed model is called the extended alpha-power exponential-AFT (EAPE-AFT) regression model. We show that the EAPE distribution is closed under the AFT model. The parameters of the proposed EAPE-AFT model have been estimated by using the method of maximum likelihood estimation. An extensive simulation study was also conducted to examine the performance of the estimates under several scenarios based on the shapes of the baseline hazard function. Finally, real-life censored survival data has been used to illustrate the applicability of the proposed model.

CLC number: 60E05, 62E99

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AIMS Mathematics
Pages 15610-15638

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Cite this article:
Kariuki V, Wanjoya A, Ngesa O, et al. The accelerated failure time regression model under the extended-exponential distribution with survival analysis. AIMS Mathematics, 2024, 9(6): 15610-15638. https://doi.org/10.3934/math.2024754

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Received: 04 February 2024
Revised: 10 April 2024
Accepted: 18 April 2024
Published: 29 April 2024
©2024 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)