不完全观测随机过程的似然推断：可验证性条件
Likelihood inference for incompletely observed stochastic processes: ignorability conditions
摘要：
我们开发了一个随机过程的框架中的似然推理的可验证性和条件的研究。我们定义了一个粗化模型的过程，其中包括离散时间的观察以及审查连续时间的观察，并适用于连续状态空间过程以及计数过程。为了准备工作，我们回忆操纵边际和条件似然比似然比（可适用于随机过程）的公式。根据两个似然比的局部相等性来定义可忽略性。我们给出了可解释性的静态条件，然后给出了更易于解释的动态条件。我们说明了使用的动态条件的审查，缺失数据和联合建模的问题中的可扩展性。
英文摘要：
We develop a study of ignorability and conditions thereof for likelihood inference in the framework of stochastic processes. We define a coarsening model for processes which includes discrete-time observations as well as censored continuous-time observations and applies to continuous state-space processes as well as counting processes. For preparing the work we recall formulas for manipulating marginal and conditional likelihood ratios (which can apply to stochastic processes). Ignorability is defined in terms of local equality of two likelihood ratios. We give static conditions of ignorability and then dynamical conditions which are more interpretable. We illustrate the use of the dynamical conditions of ignorability in problems of censoring, missing data and joint modelling.
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。
