dc.contributor.author | Umar Yusuf Madaki | |
dc.contributor.author | Muhammad Sani | |
dc.contributor.author | Ibrahim Abdullahi | |
dc.contributor | Department of Mathematics and Statistics, Faculty of Science, Yobe State University, Damaturu, Nigeria | en_US |
dc.contributor | Department of Mathematics, Faculty of Science, Federal University Dutse, Jigawa State, Nigeria | en_US |
dc.contributor | Department of Mathematical Sciences, Federal university Dutsin-Ma Katsina State, Nigeria | en_US |
dc.contributor | Department of Mathematics and Statistics, School of Mathematics and Computing, Kampala International University, Uganda | en_US |
dc.creator | Babangida Ibrahim Babura | |
dc.date.accessioned | 2023-08-16T07:13:22Z | |
dc.date.available | 2023-08-16T07:13:22Z | |
dc.date.issued | 2023-04 | |
dc.identifier.citation | Applied Mathematics and Computational Intelligence (AMCI), vol.12(1), 2023, pages 17-29 | en_US |
dc.identifier.issn | 2289-1315 (print) | |
dc.identifier.issn | 2289-1323 (online) | |
dc.identifier.uri | http://dspace.unimap.edu.my:80/xmlui/handle/123456789/79078 | |
dc.description | Link to publisher's homepage at https://amci.unimap.edu.my/ | en_US |
dc.description.abstract | Cure fraction models are usually meant for survival data that contains a proportion of non subject individuals for the event under study. In order to get an accurate estimate of the cure
fraction model, researchers often used one of two models: the mixture model or the non-mixture
model. This study presents both mixture and non-mixed cure fraction models, together with a
survival data format that is based on the beta-Weibull distribution. In this body of work, an
alternative extension to the Weibull distribution was devised for the purpose of analyzing
lifetime data. The beta-Weibull distribution is a four-parameter distribution established in this
study as an alternate extension to the Weibull distribution in lifetime data analysis. The
suggested addition allows for the inclusion of covariate analysis in the model, with parameter
estimation performed using a Bayesian approach and Gibbs sampling methods. In addition, a
simulation study was carried out to emphasize the benefits of the new development. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Engineering Mathematics, Universiti Malaysia Perlis | en_US |
dc.subject.other | Bayesian analysis | en_US |
dc.subject.other | Beta-Weibull distribution | en_US |
dc.subject.other | Cure fraction models | en_US |
dc.subject.other | Survival analysis | en_US |
dc.subject.other | MCMC algorithm | en_US |
dc.title | Cure fraction models on survival data and covariates with a Bayesian parametric estimation methods | en_US |
dc.type | Article | en_US |
dc.contributor.url | bibabura@gmail.com | en_US |