Analysis of dependent competing risks using downton’s bivariate weibull distribution

dc.contributor.authorKarunanayake, Nayanthi M.
dc.contributor.examiningcommitteeMandal, Saumen (Statistics)
dc.contributor.examiningcommitteeYang, Po (Statistics)
dc.contributor.supervisorDavies, Katherine
dc.date.accessioned2023-07-07T15:48:18Z
dc.date.available2023-07-07T15:48:18Z
dc.date.issued2023-06-22
dc.date.submitted2023-06-22T07:24:06Zen_US
dc.degree.disciplineStatisticsen_US
dc.degree.levelMaster of Science (M.Sc.)
dc.description.abstractIn this thesis, the focus is developing an inference for the Downton's bivariate Weibull (DBW) distribution, particularly in the context of dependent competing risks. The study begins by investigating the statistical properties of the DBW distribution. Next, the thesis delves into likelihood inference for the DBW distribution using complete bivariate data, specifically in the context of dependent competing risks. Additionally, moment-based estimates based on complete bivariate data are developed and examined. To assess the performance of the point and interval estimates, extensive Monte Carlo simulations are conducted. Furthermore, two real datasets are presented as illustrative examples of the application of the DBW distribution with the inferential method. These illustrations serve to demonstrate the practical relevance and usefulness of the proposed model and methods. The thesis concludes with a comprehensive evaluation of the estimation techniques and suggestions for further research in this area.
dc.description.noteOctober 2023
dc.identifier.urihttp://hdl.handle.net/1993/37402
dc.language.isoeng
dc.rightsopen accessen_US
dc.subjectDownton’s bivariate Weibull
dc.subjectlifetimes
dc.subjectcompeting risk model
dc.subjectdependent competing risks
dc.subjectlikelihood inference
dc.titleAnalysis of dependent competing risks using downton’s bivariate weibull distribution
dc.typemaster thesisen_US
local.subject.manitobano
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