EFFICIENT ESTIMATION FOR THE PROPORTIONAL HAZARDS MODEL WITH LEFT-TRUNCATED AND "CASE 1" INTERVAL-CENSORED DATA
EFFICIENT ESTIMATION FOR THE PROPORTIONAL HAZARDS MODEL WITH LEFT-TRUNCATED AND "CASE 1" INTERVAL-CENSORED DATA
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发表时间：
2003
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通讯作者：
J. S. Kim
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作者：
J. S. Kim
研究了左截尾和“情形1”区间删失数据下比例风险模型的极大似然估计。在适当的正则性条件下，回归参数的极大似然估计是渐近正态的，收敛速度为n阶，并且达到了信息界，即使基线累积风险函数的极大似然估计的左截断时间与截尾时间之差仅以n 1/3的速度收敛.考虑了两种估计回归参数极大似然估计的方差-协方差矩阵的方法。一种是基于广义信息缺失原理，另一种是基于轮廓信息过程。模拟研究表明，这两种方法的工作以及在中等规模的样本的偏差和方差。最后给出了一个例子来说明该方法。
The maximum likelihood estimator (MLE) for the proportional hazards model with left-truncated and "Case 1" interval-censored data is studied. Under appropriate regularity conditions,the MLE of the regression parameter is shown to be asymptotically normal with a root-n convergence rate and achieves the informa- tion bound,even though the difference between left-truncation time and censoring time of the MLE of the baseline cumulative hazard function converges only at rate n 1/3 . Two methods to estimate the variance-covariance matrix of the MLE of the regression parameter are considered. One is based on a generalized missing in- formation principle and the other is based on the profile information procedure. Simulation studies show that both methods work well in terms of bias and variance for samples of moderate sizes. An example is provided to illustrate the methods.