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dc.contributor.authorPavlov, Andrey
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2010-04-26 18:34:01.131en
dc.date.accessioned2010-04-27T21:36:33Z
dc.date.available2010-04-27T21:36:33Z
dc.date.issued2010-04-27T21:36:33Z
dc.identifier.urihttp://hdl.handle.net/1974/5585
dc.descriptionThesis (Ph.D, Mathematics & Statistics) -- Queen's University, 2010-04-26 18:34:01.131en
dc.description.abstractIn this study we look at the problem of analysing survival data in the presence of longitudinally collected covariates. New methodology for analysing such data has been developed through the use of hidden Markov modeling. Special attention has been given to the case of large information volume, where a preliminary data reduction is necessary. Novel graphical diagnostics have been proposed to assess goodness of fit and significance of covariates. The methodology developed has been applied to the data collected on behaviors of Mexican fruit flies, which were monitored throughout their lives. It has been found that certain patterns in eating behavior may serve as an aging marker. In particular it has been established that the frequency of eating is positively correlated with survival times.en
dc.languageenen
dc.language.isoenen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectSurvival Analysisen
dc.subjectLongitudinal Covariatesen
dc.subjectJoint Modelen
dc.subjectState Space Modelen
dc.subjectHidden Markov Modelen
dc.subjectKalman Filteren
dc.subjectEvent History Charten
dc.subjectConditional Expected Remaining Lifespanen
dc.titleA New Approach in Survival Analysis with Longitudinal Covariatesen
dc.typeThesisen
dc.description.degreePh.Den
dc.contributor.supervisorSteinsaltz, Daviden
dc.contributor.supervisorThomson, David J.en
dc.contributor.departmentMathematics and Statisticsen


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