The Extended Exponential-Weibull Accelerated Failure Time Model with Application to Sudan COVID-19 Data

Adam Braima S. Mastor (), Abdulaziz S. Alghamdi, Oscar Ngesa, Joseph Mung’atu, Christophe Chesneau () and Ahmed Z. Afify
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Adam Braima S. Mastor: Department of Mathematics (Statistics Option) Programme, Pan African University, Institute for Basic Sciences, Technology and Innovation (PAUSTI), Nairobi P.O. Box 62000-00200, Kenya
Abdulaziz S. Alghamdi: Department of Mathematics, College of Science & Arts, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia
Oscar Ngesa: Department of Mathematics and Physical Sciences, Taita Taveta University, Voi P.O. Box 635-80300, Kenya
Joseph Mung’atu: Department of Mathematics, Jomo Kenyatta University of Agriculture and Technology (JKUAT), Juja P.O. Box 62000-00200, Kenya
Christophe Chesneau: Department of Mathematics, LMNO, CNRS-Université de Caen, Campus II, Science 3, 14032 Caen, France
Ahmed Z. Afify: Department of Statistics, Mathematics and Insurance, Benha University, Benha 13511, Egypt

Mathematics, 2023, vol. 11, issue 2, 1-26

Abstract: A fully parametric accelerated failure time (AFT) model with a flexible, novel modified exponential Weibull baseline distribution called the extended exponential Weibull accelerated failure time (ExEW-AFT) model is proposed. The model is presented using the multi-parameter survival regression model, where more than one distributional parameter is linked to the covariates. The model formulation, probabilistic functions, and some of its sub-models were derived. The parameters of the introduced model are estimated using the maximum likelihood approach. An extensive simulation study is used to assess the estimates’ performance using different scenarios based on the baseline hazard shape. The proposed model is applied to a real-life right-censored COVID-19 data set from Sudan to illustrate the practical applicability of the proposed AFT model.

Keywords: baseline hazard; survival regression model; maximum likelihood; Monte Carlo simulation; COVID-19 data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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