Efficient estimation of the censored linear regression model
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:...
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| Veröffentlicht in: | Biometrika 2013-06, Vol.100 (2), p.525-530 |
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| container_title | Biometrika |
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| creator | LIN, YUANYUAN CHEN, KANI |
| description | 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. |
| doi_str_mv | 10.1093/biomet/ass073 |
| format | Article |
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| ispartof | Biometrika, 2013-06, Vol.100 (2), p.525-530 |
| issn | 0006-3444 1464-3510 |
| language | eng |
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| source | JSTOR Mathematics & Statistics; JSTOR Archive Collection A-Z Listing; Oxford University Press Journals All Titles (1996-Current); Alma/SFX Local Collection |
| subjects | Asymptotic methods Estimating techniques Mathematical models Miscellanea Numerical analysis Regression analysis Studies |
| title | Efficient estimation of the censored linear regression model |
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