A Marked Point Process Approach to Censored Failure Data with Complicated Covariates
将复杂失效时间数据视为标记点过程的样本路径，通过将标记点拆分为创新和非创新两部分，推导出参数统计模型的似然表达式，适用于含随机协变量、删失观测和多次失效的数据。
Complicated failure time data which can involve, e.g., random covariates, censored observations and multiple failures, is here considered as a sample path of a marked point process (MPP). Our main task is to derive likelihood expressions for parametric statistical models under such general circumstances. To do this, and motivated by concrete examples, we split each marked point into two characteristic parts, called innovation and non-innovation, and then characterize this representation in terms of the statistical model. Technically the paper is based on the martingale approach to point processes.