We introduce a model for recurrent event data subject to left-, right-, and intermittent-censoring. The observations consist of binary sequences (along with covariates) for each individual under study. These sequences are modeled as generated by latent Ornstein–Uhlenbeck processes being above or below certain thresholds. Features of the latent process and the thresholds are taken as functions of covariates, allowing the researcher to distinguish factors that have an effect on the frailty, from those that have an effect on the variability, of the observational unit. Inference is achieved by a quasi-likelihood approach, for which consistency and asymptotic normality is established. An advantage of our model is that particularities regarding the censoring need not be taken actively into account, and that it is well suited for situations where the individuals under study are irregularly and asynchronously observed. The motivation for our model came from a dataset pertaining to the incidence of diarrhoea among Brazilian children growing up under rather harsh conditions. We analyze these data with our model and contrast the results with an intensity-based counting process analysis of the same data.

中文翻译：

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通过裁剪的 Ornstein-Uhlenbeck 过程对开-关数据进行模型和推理

我们引入了一个受左审查、右审查和间歇审查的循环事件数据模型。观察结果由所研究的每个个体的二进制序列（以及协变量）组成。这些序列被建模为由高于或低于某些阈值的潜在 Ornstein-Uhlenbeck 过程生成。潜在过程的特征和阈值被视为协变量的函数，使研究人员能够区分对观察单位的脆弱性有影响的因素和对可变性有影响的因素。推理是通过拟似然方法实现的，为此建立了一致性和渐近正态性。我们模型的一个优点是不需要积极考虑审查的特殊性，并且它非常适合被研究的个体被不规则和异步观察的情况。我们模型的动机来自一个关于在相当恶劣的条件下长大的巴西儿童腹泻发病率的数据集。我们使用我们的模型分析这些数据，并将结果与​​相同数据的基于强度的计数过程分析进行对比。