Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85024
Title: Generalized M-estimation for the accelerated failure time model
Authors: Wang, Siyang
Hu, Tao
Xiang, Liming
Cui, Hengjian
Keywords: Accelerated failure time model
Generalized M-estimator
Issue Date: 2015
Source: Wang, S., Hu, T., Xiang, L., & Cui, H. (2015). Generalized M-estimation for the accelerated failure time model. Statistics, 50(1), 114-138.
Series/Report no.: Statistics
Abstract: The accelerated failure time (AFT) model is an important regression tool to study the association between failure time and covariates. In this paper, we propose a robust weighted generalized M (GM) estimation for the AFT model with right-censored data by appropriately using the Kaplan–Meier weights in the GM–type objective function to estimate the regression coefficients and scale parameter simultaneously. This estimation method is computationally simple and can be implemented with existing software. Asymptotic properties including the root-n consistency and asymptotic normality are established for the resulting estimator under suitable conditions. We further show that the method can be readily extended to handle a class of nonlinear AFT models. Simulation results demonstrate satisfactory finite sample performance of the proposed estimator. The practical utility of the method is illustrated by a real data example.
URI: https://hdl.handle.net/10356/85024
http://hdl.handle.net/10220/43609
ISSN: 0233-1888
DOI: 10.1080/02331888.2015.1032970
Rights: © 2015 Taylor & Francis. This is the author created version of a work that has been peer reviewed and accepted for publication by Statistics, Taylor & Francis. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1080/02331888.2015.1032970].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SPMS Journal Articles

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