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dc.contributor.authorBoruvka, Audrey
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2007-07-18 22:13:13.243en
dc.date.accessioned2007-08-02T15:47:33Z
dc.date.available2007-08-02T15:47:33Z
dc.date.issued2007-08-02T15:47:33Z
dc.identifier.urihttp://hdl.handle.net/1974/489
dc.descriptionThesis (Master, Mathematics & Statistics) -- Queen's University, 2007-07-18 22:13:13.243en
dc.description.abstractThe proportional hazards model developed by Cox (1972) is by far the most widely used method for regression analysis of censored survival data. Application of the Cox model to more general event history data has become possible through extensions using counting process theory (e.g., Andersen and Borgan (1985), Therneau and Grambsch (2000)). With its development based entirely on counting processes, Aalen’s additive risk model offers a flexible, nonparametric alternative. Ordinary least squares, weighted least squares and ridge regression have been proposed in the literature as estimation schemes for Aalen’s model (Aalen (1989), Huffer and McKeague (1991), Aalen et al. (2004)). This thesis develops data-driven parameter selection criteria for the weighted least squares and ridge estimators. Using simulated survival data, these new methods are evaluated against existing approaches. A survey of the literature on the additive risk model and a demonstration of its application to real data sets are also provided.en
dc.format.extent4570625 bytes
dc.format.mimetypeapplication/pdf
dc.languageenen
dc.language.isoenen
dc.relation.ispartofseries"Canadian theses"en
dc.rights"This 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.subjectAalen's additive modelen
dc.subjectBandwidth selectionen
dc.subjectData-driven estimationen
dc.subjectEvent history analysisen
dc.subjectGeneralized cross-validationen
dc.subjectl-curveen
dc.subjectRidge regressionen
dc.subjectWeighted least squaresen
dc.titleData-driven estimation for Aalen's additive risk modelen
dc.typethesisen
dc.description.degreeMasteren
dc.contributor.supervisorTu, Dongshengen
dc.contributor.supervisorTakahara, Glenen
dc.contributor.departmentMathematics and Statisticsen


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