Please use this identifier to cite or link to this item:
|Title:||A general semiparametric hazards regression model: Efficient estimation and structure selection||Authors:||Tong, X.
|Keywords:||Accelerated failure time model
Cox proportional hazards model
Kernel-smoothed profile likelihood function
|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. https://doi.org/10.1002/sim.5885||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/104929||ISSN:||02776715||DOI:||10.1002/sim.5885|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.