Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75726
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Title: Sieve maximum likelihood estimation for a general class of accelerated hazards models with bundled parameters
Authors: Zhao, XQ 
Wu, YS
Yin, GS
Issue Date: 2017
Source: Bernoulli, 2017, v. 23, no. 4B, p. 3385-3411
Abstract: In semiparametric hazard regression, nonparametric components may involve unknown regression parameters. Such intertwining effects make model estimation and inference much more difficult than the case in which the parametric and nonparametric components can be separated out. We study the sieve maximum likelihood estimation for a general class of hazard regression models, which include the proportional hazards model, the accelerated failure time model, and the accelerated hazards model. Coupled with the cubic B-spline, we propose semiparametric efficient estimators for the parameters that are bundled inside the non parametric component. We overcome the challenges due to intertwining effects of the bundled parameters, and establish the consistency and asymptotic normality properties of the estimators. We carry out simulation studies to examine the finite-sample properties of the proposed method, and demonstrate its efficiency gain over the conventional estimating equation approach. For illustration, we apply our proposed method to a study of bone marrow transplantation for patients with acute leukemia.
Keywords: Accelerated failure time model
B-spline
Proportional hazards model
Semiparametric efficiency bound
Sieve maximum likelihood estimator
Survival data
Publisher: International Statistical Institute
Journal: Bernoulli 
ISSN: 1350-7265
EISSN: 1573-9759
DOI: 10.3150/16-BEJ850
Rights: © 2017 ISI/BS
The following publication Xingqiu Zhao, Yuanshan Wu, Guosheng Yin "Sieve maximum likelihood estimation for a general class of accelerated hazards models with bundled parameters," Bernoulli, Bernoulli 23(4B), 3385-3411, (November 2017) is available at https://doi.org/10.3150/16-BEJ850.
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