EMPIRICAL LIKELIHOOD REGRESSION ANALYSIS FOR RIGHT CENSORED DATA
EMPIRICAL LIKELIHOOD REGRESSION ANALYSIS FOR RIGHT CENSORED DATA
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发表时间：
2003
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通讯作者：
Gang Li;Qihua Wang
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作者：
Gang Li;Qihua Wang
对于分析删失回归数据，线性模型是考克斯（1972）比例风险模型的有用替代。本文针对右删失数据的线性回归分析发展了经验似然方法。利用一种合成数据方法，为回归系数向量构建了一种调整的经验似然。结果表明，调整后的经验似然具有中心卡方极限分布，这使得人们能够使用标准卡方表进行推断。我们还推导了一种针对回归系数线性组合的调整经验似然方法。此外，我们讨论了如何纳入辅助信息。进行了一项小型模拟研究，以突出调整后的经验似然方法与传统正态近似方法相比的性能。结果表明，经验似然置信区间往往比正态理论区间具有更准确的覆盖概率。使用斯坦福心脏移植数据给出了一个示例。
Linear models are useful alternatives to the Cox (1972) proportional haz- ards model for analyzing censored regression data. This article develops empirical likelihood methods for linear regression analysis of right censored data. An adjusted empirical likelihood is constructed for the vector of regression coecien ts using a synthetic data approach. The adjusted empirical likelihood is shown to have a cen- tral chi-squared limiting distribution, which enables one to make inference using standard chi-square tables. We also derive an adjusted empirical likelihood method for linear combinations of the regression coecien ts. In addition, we discuss how to incorporate auxiliary information. A small simulation study is carried out to highlight the performance of the adjusted empirical likelihood methods compared with the traditional normal approximation method. It shows that the empirical likelihood condence intervals tend to have more accurate coverage probabilities than the normal theroy intervals. An illustration is given using the Stanford Heart Transplant data.