Conditional Aalen–Johansen estimation
提出条件Aalen-Johansen估计量，用于非参数估计条件状态占据概率，适用于有限状态跳跃过程，支持外部和内部协变量，在生存模型中退化为条件Kaplan-Meier估计量，并建立了强一致性和渐近正态性。
Abstract The conditional Aalen–Johansen estimator, a general‐purpose nonparametric estimator of conditional state occupation probabilities, is introduced. The estimator is applicable for any finite‐state jump process and supports conditioning on external as well as internal covariate information. The conditioning feature permits for a much more detailed analysis of the distributional characteristics of the process. The estimator reduces to the conditional Kaplan–Meier estimator in the special case of a survival model and also englobes other, more recent, landmark estimators when covariates are discrete. Strong uniform consistency and asymptotic normality are established under lax moment conditions on the multivariate counting process, allowing in particular for an unbounded number of transitions.