Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/57258

Efficient estimation of the censored linear regression model

Authors Lin, Yuanyuan
Chen, Kani View this author's profile
Issue Date 2013
Source Biometrika , v. 100, (2), June 2013, p. 525-530
Summary 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.
Subjects
ISSN 0006-3444
Language English
Format Article
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