Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33590
Title: Efficient estimation of the censored linear regression model
Authors: Lin, Y
Chen, K
Keywords: Counting process martingale
Linear regression model
One-step efficient estimation
Issue Date: 2013
Publisher: Oxford University Press
Source: Biometrika, 2013, v. 100, no. 2, p. 525-530 How to cite?
Journal: Biometrika 
Abstract: In linear regression or accelerated failure time models, complications in efficient estimation arise from the multiple roots of the efficient score and density estimation. This paper proposes a one-step efficient estimation method based on a counting process martingale, which has several advantages: it avoids the multiple-root problem, the initial estimator is easily available and the variance estimator can be obtained by employing plug-in rules. A simple and effective data-driven bandwidth selector is provided. The proposed estimator is proved to be semiparametric efficient, with the same asymptotic variance as the efficient estimator when the error distribution is known up to a location shift. Numerical studies with supportive evidence are presented. The proposal is applied to the Colorado Plateau uranium miners data.
URI: http://hdl.handle.net/10397/33590
ISSN: 0006-3444
EISSN: 1464-3510
DOI: 10.1093/biomet/ass073
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