Extended Weighted Log-Rank Estimating Functions in Censored Regression
在删失回归模型中，利用加权积分对数秩估计函数引入回归估计量，其协方差矩阵不依赖误差密度且可可靠估计，便于推断；部分估计量在重要子模型下渐近效率高，并推导了拟合不足检验，数值表现良好。
Abstract In the censored regression model, some regression estimators are introduced using weighted integrals of the log-rank estimating functions. Their limiting covariance matrices do not involve the error density and can be reliably estimated. Inference can then be easily obtained. Some of these estimators have high asymptotic efficiency at some important submodels. Some lack-of-fit tests are derived that require little extra computing time. These tests are asymptotically normal under the model and consistent against certain monotone or convex model misspecifications. Numerical studies show that for some weight functions, the estimators and tests perform well. Implementation of the proposed procedures is discussed and illustrated in a real data example.