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Title: Model selection in nonparametric hazard regression
Authors: Leng, C. 
Zhang, H.H.
Keywords: COSSO
Cox proportional hazard model
Model selection
Penalized likelihood
Issue Date: Oct-2006
Citation: Leng, C., Zhang, H.H. (2006-10). Model selection in nonparametric hazard regression. Journal of Nonparametric Statistics 18 (7-8) : 417-429. ScholarBank@NUS Repository.
Abstract: We propose a novel model selection method for a nonparametric extension of the Cox proportional hazard model, in the framework of smoothing splines ANOVA models. The method automates the model building and model selection processes simultaneously by penalizing the reproducing kernel Hilbert space norms. On the basis of a reformulation of the penalized partial likelihood, we propose an efficient algorithm to compute the estimate. The solution demonstrates great flexibility and easy interpretability in modeling relative risk functions for censored data. Adaptive choice of the smoothing parameter is discussed. Both simulations and a real example suggest that our proposal is a useful tool for multivariate function estimation and model selection in survival analysis.
Source Title: Journal of Nonparametric Statistics
ISSN: 10485252
DOI: 10.1080/10485250601027042
Appears in Collections:Staff Publications

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