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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/489

Title: Data-driven estimation for Aalen's additive risk model
Authors: Boruvka, Audrey

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Keywords: Aalen's additive model
Bandwidth selection
Data-driven estimation
Event history analysis
Generalized cross-validation
l-curve
Ridge regression
Weighted least squares
Issue Date: 2007
Series/Report no.: "Canadian theses"
Abstract: The 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.
Description: Thesis (Master, Mathematics & Statistics) -- Queen's University, 2007-07-18 22:13:13.243
URI: http://hdl.handle.net/1974/489
Appears in Collections:Mathematics & Statistics Graduate Theses
Queen's Theses & Dissertations

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