On least-squares regression with censored data
On least-squares regression with censored data
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DOI：
10.1093/biomet/93.1.147
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发表时间：
2006-03-01
期刊：
影响因子：
2.7
通讯作者：
Ying, ZL
中科院分区：
文献类型：
--
作者：
Jin, ZZ;Lin, DY;Ying, ZL
半参数加速失效时间模型将失效时间的对数与协变量线性关联，而不指定误差分布。本文给出了该模型在右删失数据下基于最小二乘原理的简单可靠的推断方法。所提出的向量值回归参数估计是Buckley-James估计方程的迭代解，其初始值为一致估计。证明了估计量是一致的，并且是渐近正态的。提出了一种新的估计极限协方差矩阵的重采样方法。考虑了多变量失效时间数据的边际模型的扩展。通过仿真研究对新推理过程的性能进行了评估。提供了医学研究的插图。
The semiparametric accelerated failure time model relates the logarithm of the failure time linearly to the covariates while leaving the error distribution unspecified. The present paper describes simple and reliable inference procedures based on the least-squares principle for this model with right-censored data. The proposed estimator of the vector-valued regression parameter is an iterative solution to the Buckley-James estimating equation with a preliminary consistent estimator as the starting value. The estimator is shown to be consistent and asymptotically normal. A novel resampling procedure is developed for the estimation of the limiting covariance matrix. Extensions to marginal models for multivariate failure time data are considered. The performance of the new inference procedures is assessed through simulation studies. Illustrations with medical studies are provided.