Non-parametric estimators of scaled cash flows
针对多状态人寿保险中因保单持有人行为产生的预期现金流，提出一种缩放版Aalen-Johansen估计量，在随机右删失下具有强一致性和渐近正态性，模拟表现优于已有方法。
In multi-state life insurance, incidental policyholder behavior gives rise to expected cash flows that are not easily targeted by classic non-parametric estimators if data is subject to sampling effects. We introduce a scaled version of the classic Aalen–Johansen estimator that overcomes this challenge. Strong uniform consistency and asymptotic normality are established under entirely random right-censoring, subject to lax moment conditions on the multivariate counting process. In a simulation study, the estimator outperforms earlier proposals from the literature. Finally, we showcase the potential of the presented method to other areas of actuarial science.