Generalized M-estimation for the accelerated failure time model
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...
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sg-ntu-dr.10356-850242023-02-28T19:31:06Z Generalized M-estimation for the accelerated failure time model Wang, Siyang Hu, Tao Xiang, Liming Cui, Hengjian School of Physical and Mathematical Sciences Accelerated failure time model Generalized M-estimator 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. MOE (Min. of Education, S’pore) Accepted version 2017-08-18T01:53:06Z 2019-12-06T15:55:47Z 2017-08-18T01:53:06Z 2019-12-06T15:55:47Z 2015 Journal Article Wang, S., Hu, T., Xiang, L., & Cui, H. (2015). Generalized M-estimation for the accelerated failure time model. Statistics, 50(1), 114-138. 0233-1888 https://hdl.handle.net/10356/85024 http://hdl.handle.net/10220/43609 10.1080/02331888.2015.1032970 en Statistics © 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]. 37 p. application/pdf |
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Accelerated failure time model Generalized M-estimator Wang, Siyang Hu, Tao Xiang, Liming Cui, Hengjian Generalized M-estimation for the accelerated failure time model |
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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. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Wang, Siyang Hu, Tao Xiang, Liming Cui, Hengjian |
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Article |
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Wang, Siyang Hu, Tao Xiang, Liming Cui, Hengjian |
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Wang, Siyang |
title |
Generalized M-estimation for the accelerated failure time model |
title_short |
Generalized M-estimation for the accelerated failure time model |
title_full |
Generalized M-estimation for the accelerated failure time model |
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Generalized M-estimation for the accelerated failure time model |
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Generalized M-estimation for the accelerated failure time model |
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generalized m-estimation for the accelerated failure time model |
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2017 |
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https://hdl.handle.net/10356/85024 http://hdl.handle.net/10220/43609 |
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