Efficient estimation of the censored linear regression model
Efficient estimation of the censored linear regression model
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DOI：
10.1093/biomet/ass073
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发表时间：
2013-06-01
期刊：
影响因子：
2.7
通讯作者：
Chen, Kani
中科院分区：
文献类型：
--
作者：
Lin, Yuanyuan;Chen, Kani
在线性回归或加速失效时间模型中，效率估计的复杂性来自于效率得分和密度估计的多重根。提出了一种基于计数过程鞅的一步有效估计方法，该方法避免了重根问题，初始估计量容易获得，方差估计量可采用插入规则得到.提供了一种简单有效的数据驱动带宽选择器。该估计被证明是半参数有效的，具有相同的渐近方差的有效估计时，误差分布是已知的位置移动。数值研究与支持性证据。该建议适用于科罗拉多高原铀矿数据。
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.