An eigenfunction approach to multivariate and high-dimensional survival analysis

生存分析是对所谓事件时间数据的研究，通常多变量生存数据要么是平行数据，即试图观察几个相关个体（如家庭成员、人体器官、系统组成部分）感兴趣的事件（如疾病发作、死亡、衰竭）的时间，要么是串行数据，即跟踪一系列连续事件（如疾病的阶段），或者可能是这两种方案的组合。通常，事件发生时间数据与高维协变量相结合，例如从基因表达数据中获得的遗传标记信息。事件数据时间复杂性的两个主要来源是

Survival analysis is the study of so-called time-to-event data, and typically multivariate survival data arise either as parallel data, where one tries to observe times to events of interest (such as disease onset, death, failure) for several related individuals (such as family members, organs of human body, components of a system), or serial data, where a series of successive events are tracked (such as stages of a disease), or possibly as a combination of these two schemes. Often time to event data is combined with high-dimensional covariates, such as information on genetic markers available from gene expression data. Two major sources of complication in time to event data are