Please use this identifier to cite or link to this item: https://doi.org/10.1198/jasa.2009.tm09372
Title: Dimension reduction and semiparametric estimation of survival models
Authors: Xia, Y. 
Zhang, D.
Xu, J. 
Keywords: Censored data
Hazard function
Linear transformation model
Nonparametric regression
Issue Date: Mar-2010
Citation: Xia, Y., Zhang, D., Xu, J. (2010-03). Dimension reduction and semiparametric estimation of survival models. Journal of the American Statistical Association 105 (489) : 278-290. ScholarBank@NUS Repository. https://doi.org/10.1198/jasa.2009.tm09372
Abstract: In this paper, we propose a new dimension reduction method by introducing a nominal regression model with the hazard function as the conditional mean, which naturally retrieves information from complete data and censored data as well. Moreover, without requiring the linearity condition, the new method can estimate the entire central subspace consistently and exhaustively. The method also provides an alternative approach for the analysis of censored data assuming neither the link function nor the distribution. Hence, it exhibits superior robustness properties. Numerical studies show that the method can indeed be readily used to efficiently estimate survival models, explore the data structures and identify important variables. © 2010 American Statistical Association.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105093
ISSN: 01621459
DOI: 10.1198/jasa.2009.tm09372
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