条件 Aalen–Johansen 估计
Conditional Aalen--Johansen estimation
AI总结：
提出适用于任意有限状态跳过程、可纳入内外部协变量的条件 Aalen–Johansen 非参数估计量，并证明其强一致一致性与渐近正态性。
摘要：
本文介绍了条件 Aalen–Johansen 估计量，这是一种用于估计条件状态占据概率的通用非参数估计量。该估计量适用于任意有限状态跳过程，并支持基于外部协变量信息以及内部协变量信息进行条件化。这一条件化特性允许对过程的分布特征进行细致得多的分析。在生存模型的特殊情形下，该估计量退化为条件 Kaplan–Meier 估计量；当协变量为离散时，它还包含了其他较新的里程碑估计量。在多元计数过程的宽松矩条件下，本文建立了该估计量的强一致一致性和渐近正态性，尤其允许转移次数无界。
英文摘要：
The conditional Aalen--Johansen estimator, a general-purpose non-parametric 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
