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Title: A general semiparametric hazards regression model: Efficient estimation and structure selection
Authors: Tong, X.
Zhu, L.
Leng, C. 
Leisenring, W.
Robison, L.L.
Keywords: Accelerated failure time model
Cox proportional hazards model
Kernel-smoothed profile likelihood function
Model selection
Penalized likelihood
Issue Date: 10-Dec-2013
Citation: Tong, X., Zhu, L., Leng, C., Leisenring, W., Robison, L.L. (2013-12-10). A general semiparametric hazards regression model: Efficient estimation and structure selection. Statistics in Medicine 32 (28) : 4980-4994. ScholarBank@NUS Repository.
Abstract: We consider a general semiparametric hazards regression model that encompasses the Cox proportional hazards model and the accelerated failure time model for survival analysis. To overcome the nonexistence of the maximum likelihood, we derive a kernel-smoothed profile likelihood function and prove that the resulting estimates of the regression parameters are consistent and achieve semiparametric efficiency. In addition, we develop penalized structure selection techniques to determine which covariates constitute the accelerated failure time model and which covariates constitute the proportional hazards model. The proposed method is able to estimate the model structure consistently and model parameters efficiently. Furthermore, variance estimation is straightforward. The proposed estimation performs well in simulation studies and is applied to the analysis of a real data set. © 2013 John Wiley & Sons, Ltd.
Source Title: Statistics in Medicine
ISSN: 02776715
DOI: 10.1002/sim.5885
Appears in Collections:Staff Publications

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