BACKWARD ESTIMATION OF STOCHASTIC PROCESSES WITH FAILURE EVENTS AS TIME ORIGINS.
BACKWARD ESTIMATION OF STOCHASTIC PROCESSES WITH FAILURE EVENTS AS TIME ORIGINS.
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DOI：
10.1214/09-aoas319
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发表时间：
2010-09-01
期刊：
影响因子：
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通讯作者：
Wang MC
中科院分区：
文献类型：
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作者：
Gary Chan KC;Wang MC
随机过程通常会在某些故障事件发生之前的短时间内表现出突然的系统性变化。例如，死亡前医疗费用的增加以及艾滋病诊断前 CD4 计数的减少。为了研究随机过程的这种终端行为，一种自然而直接的方法是使用故障事件作为时间原点来调整过程。本文研究了从故障事件开始向后计算时间的后向随机过程，并提出了在后续受到左截断和右删失的情况下对后向过程均值的单样本非参数估计。我们将讨论包括流行的队列数据以扩大所提议的估计量的可识别区域和大样本属性以及相关扩展的好处。 SEER-Medicare 关联数据集用于说明所提出的方法。
Stochastic processes often exhibit sudden systematic changes in pattern a short time before certain failure events. Examples include increase in medical costs before death and decrease in CD4 counts before AIDS diagnosis. To study such terminal behavior of stochastic processes, a natural and direct way is to align the processes using failure events as time origins. This paper studies backward stochastic processes counting time backward from failure events, and proposes one-sample nonparametric estimation of the mean of backward processes when follow-up is subject to left truncation and right censoring. We will discuss benefits of including prevalent cohort data to enlarge the identifiable region and large sample properties of the proposed estimator with related extensions. A SEER–Medicare linked data set is used to illustrate the proposed methodologies.