用于去偏Cox回归的Wild Bootstrap与Efron's Bootstrap
Wild Bootstrap and Efron's Bootstrap for Debiased Cox Regression
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中文总结 AI 辅助
该研究针对Cox Lasso选择后去偏Cox估计量的系数推断问题，提出Wild Bootstrap与Efron Bootstrap两种方法，经模拟验证其渐近有效性与有限样本表现，可提升区间覆盖准确性，并通过SUPPORT2数据集示例说明应用。
中文摘要 AI 辅助
带Lasso惩罚的Cox回归被广泛用于生存时间数据的变量选择，但选择后进行可靠的系数推断仍存在困难。我们研究Cox Lasso选择后去偏Cox估计量的自助法推断，考虑两种基于得分的方法：使用独立乘子的Wild Bootstrap，以及使用中心化多项重抽样权重的Efron Bootstrap。两种方法均保持原始Cox Lasso拟合和去偏矩阵固定，将自助权重应用于决定去偏估计量一阶分布的得分贡献。我们证明两种自助法的渐近有效性，并通过大量模拟研究其有限样本表现。在许多小样本和中等样本设置中，自助法区间比一阶去偏Wald区间的覆盖准确性更高，改进幅度取决于模拟设置和调优规则。通过SUPPORT2数据集的实际应用说明了这些方法的使用。
英文摘要
Cox regression with Lasso penalization is widely used for variable selection in time-to-event data, but reliable coefficient inference after selection remains difficult. We investigate bootstrap inference for the debiased Cox estimator after Cox Lasso selection. Two score-based procedures are considered: a wild bootstrap using independent multipliers and an Efron bootstrap using centered multinomial resampling weights. Both procedures keep the original Cox Lasso fit and debiasing matrix fixed and apply bootstrap weights to the score contributions that determine the first-order distribution of the debiased estimator. We establish asymptotic validity for both bootstrap schemes and study their finite-sample behavior in extensive simulations. The bootstrap intervals improve coverage accuracy over first-order debiased Wald intervals in many small- and moderate-sample settings, with the size of the improvement depending on the simulation setting and tuning rule. A SUPPORT2 application illustrates the procedures in practice.
