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dc.contributor.authorUmar Yusuf Madaki
dc.contributor.authorMuhammad Sani
dc.contributor.authorIbrahim Abdullahi
dc.contributorDepartment of Mathematics and Statistics, Faculty of Science, Yobe State University, Damaturu, Nigeriaen_US
dc.contributorDepartment of Mathematics, Faculty of Science, Federal University Dutse, Jigawa State, Nigeriaen_US
dc.contributorDepartment of Mathematical Sciences, Federal university Dutsin-Ma Katsina State, Nigeriaen_US
dc.contributorDepartment of Mathematics and Statistics, School of Mathematics and Computing, Kampala International University, Ugandaen_US
dc.creatorBabangida Ibrahim Babura
dc.date.accessioned2023-08-16T07:13:22Z
dc.date.available2023-08-16T07:13:22Z
dc.date.issued2023-04
dc.identifier.citationApplied Mathematics and Computational Intelligence (AMCI), vol.12(1), 2023, pages 17-29en_US
dc.identifier.issn2289-1315 (print)
dc.identifier.issn2289-1323 (online)
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/79078
dc.descriptionLink to publisher's homepage at https://amci.unimap.edu.my/en_US
dc.description.abstractCure 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.isoenen_US
dc.publisherInstitute of Engineering Mathematics, Universiti Malaysia Perlisen_US
dc.subject.otherBayesian analysisen_US
dc.subject.otherBeta-Weibull distributionen_US
dc.subject.otherCure fraction modelsen_US
dc.subject.otherSurvival analysisen_US
dc.subject.otherMCMC algorithmen_US
dc.titleCure fraction models on survival data and covariates with a Bayesian parametric estimation methodsen_US
dc.typeArticleen_US
dc.contributor.urlbibabura@gmail.comen_US


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