We study the conditional expert Kaplan-Meier estimator, an extension of the classical Kaplan--Meier estimator designed for time-to-event data subject to both right-censoring and contamination. Such contamination, where observed events may not reflect true outcomes, is common in applied settings, including insurance and credit risk, where expert opinion is often used to adjudicate uncertain events. Building on previous work, we develop a comprehensive asymptotic theory for the conditional version incorporating covariates through kernel smoothing. We establish functional consistency and weak convergence under suitable regularity conditions and quantify the bias induced by imperfect expert information. The results show that unbiased expert judgments ensure consistency, while systematic deviations lead to a deterministic asymptotic bias that can be explicitly characterized. We examine finite-sample properties through simulation studies and illustrate the practical use of the estimator with an application to loan default data.

翻译：本研究探讨条件专家Kaplan-Meier估计量，该方法是经典Kaplan-Meier估计量的扩展，适用于同时存在右删失和事件污染的时间-事件数据。此类污染现象（观测事件可能无法反映真实结果）常见于保险与信用风险等应用场景，其中常借助专家意见对不确定事件进行裁定。基于前人研究，我们通过核平滑方法结合协变量，为条件估计量建立了完整的渐近理论体系。在适当的正则性条件下，我们证明了函数一致性及弱收敛性，并量化了专家信息不完善导致的偏差。研究结果表明：无偏的专家判断能保证估计的一致性，而系统性偏差将产生可显式表征的确定性渐近偏差。我们通过模拟研究检验了有限样本性质，并利用贷款违约数据展示了该估计量的实际应用价值。