Please use this identifier to cite or link to this item: https://cris.library.msu.ac.zw//handle/11408/6484
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dc.contributor.authorChipo Zidanaen_US
dc.contributor.authorWhatmore Sengwenien_US
dc.contributor.authorBroderick O. Oluyedeen_US
dc.contributor.authorFastel Chipepaen_US
dc.date.accessioned2024-12-12T06:45:38Z-
dc.date.available2024-12-12T06:45:38Z-
dc.date.issued2024-07-
dc.identifier.urihttps://cris.library.msu.ac.zw//handle/11408/6484-
dc.description.abstractThe study introduces a new generalised family of distributions for cancer data modelling using a generalisation of the gamma function and a Topp-Leone-G distribution called the Gamma-Odd Topp-Leone-G (GOTL-G). Cancer data is normally characterised by complex heterogeneous properties like skewness, kurtosis, and presence of extreme values which makes it difficult to model using classical distributions. We derived multiple statistical properties including the linear representation, Re«yi entropy, quantile functions, distribution of order statistics, and maximum likelihood estimates which normally guarantees a positive effect on the generalisability of cancer data. Interestingly, we observed that these derived statistical properties make it possible for the generalisation of different models which are useful in the analysis, control, insurance, and survival of cancer patients. Our results show that this new family of distributions can be applied to a variety of data sets such as bladder and breast cancer data which exhibited high level of skewness and kurtosis as well as symmetric attributes. Therefore, we can conclude that the GOTL-G family of distributions can be extremely useful in capturing distinct complex heterogeneous properties normally exhibited by cancer patients. We recommend that this new family of distributions can be useful in modelling complex real-life applications including cancer data.en_US
dc.language.isoenen_US
dc.publisherUniversidad Nacional de Colombiaen_US
dc.relation.ispartofRevista Colombiana de Estadísticaen_US
dc.subjectExponentiated general distributionen_US
dc.subjectGamma functionen_US
dc.subjectMaximum likelihood estimationen_US
dc.subjectTopp-Leoneen_US
dc.subjectCancer modellingen_US
dc.titleCancer Data Modelling: Application of the Gamma-Odd Topp-Leone-G Family of Distributionsen_US
dc.typeresearch articleen_US
dc.identifier.doihttps://www.researchgate.net/profile/Fastel-Chipepa/publication/382305411_Cancer_Data_Modelling_Application_of_the_Gamma-Odd_Topp-Leone-G_Family_of_Distributions/links/669782208dca9f441b83051b/Cancer-Data-Modelling-Application-of-the-Gamma-Odd-Topp-Leone-G-Family-of-Distributions.pdf-
dc.contributor.affiliationDepartment of Mathematics and Statistical Sciences, Faculty of Science, Botswana International University of Science and Technology, Palapye, Botswanaen_US
dc.contributor.affiliationDepartment of Applied Mathematics and Statistics, Faculty of Science, Midlands State University, Gweru, Zimbabween_US
dc.contributor.affiliationDepartment of Mathematics and Statistical Sciences, Faculty of Science, Botswana International University of Science and Technology, Palapye, Botswanaen_US
dc.contributor.affiliationDepartment of Mathematics and Statistical Sciences, Faculty of Science, Botswana International University of Science and Technology, Palapye, Botswanaen_US
dc.relation.issn2389-8976en_US
dc.description.volume47en_US
dc.description.issue2en_US
dc.description.startpage355en_US
dc.description.endpage383en_US
item.grantfulltextopen-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
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item.cerifentitytypePublications-
item.openairetyperesearch article-
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