ABSTRACT Asymptotic properties, both consistency and weak convergence, of estimators arising in a general class of dynamic recurrent event models are presented. The class of models take into account the impact of interventions after each event occurrence, the impact of accumulating event occurrences, the induced informative and dependent right-censoring mechanism due to the data-accrual scheme, and the effect of covariate processes on the recurrent event occurrences. The class of models subsumes as special cases many of the recurrent event models that have been considered in biostatistics, reliability, and in the social sciences. The asymptotic properties presented have the potential of being useful in developing goodness-of-fit and model validation procedures, confidence intervals and confidence bands constructions, and hypothesis testing procedures for the finite- and infinite-dimensional parameters of a general class of dynamic recurrent event models, albeit the models without frailties.

中文翻译：

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一类动态重复事件模型的渐近性

摘要 介绍了在一般类别的动态循环事件模型中出现的估计量的渐近特性，包括一致性和弱收敛性。该类模型考虑了每次事件发生后干预的影响、累积事件发生的影响、由于数据累积方案引起的信息性和相关权利审查机制，以及协变量过程对重复事件的影响发生。模型类别包含许多在生物统计学、可靠性和社会科学中考虑过的重复事件模型作为特殊情况。所呈现的渐近特性有可能有助于开发拟合优度和模型验证程序、置信区间和置信带构建，