AI 中文总结
研究在线变点检测中误报控制问题，提出顺序族错误率及基于模拟的校准程序来估计监测阈值，通过模拟研究和实例验证该方法能有效控制误报，优于常用替代方法。
AI 中文摘要
在线变点检测是实时识别时间序列数据中分布变化的过程。在移动健康等应用中，新数据到达时经常进行重复测试，产生多重检验问题。传统控制族错误率（FWER）的方法不适用于此，因为检验高度相关且检验次数不预先固定。本文引入顺序族错误率（sFWER），定义为移动监测窗口内至少一个误报的概率。提出基于模拟的校准程序来估计监测阈值以将sFWER控制在期望水平。通过模拟研究表明该程序实现了期望的错误控制，而常用方法要么过于保守要么无法充分控制误报。最后用来自情感不稳定青少年和年轻人队列的被动收集的智能手机数据说明了该方法。
英文摘要
Online change point detection is the process of identifying distributional changes in time-ordered data in real time. In applications such as mobile health (mHealth), repeated testing is often performed as new data arrive, creating a multiple testing problem. Traditional approaches for controlling the family-wise error rate (FWER) are not well suited to this setting because the tests are highly dependent and the number of tests is not fixed in advance. In this work, we introduce a sequential family-wise error rate (sFWER), defined as the probability of at least one false positive within a moving monitoring window. We propose a simulation-based calibration procedure to estimate monitoring thresholds that control the sFWER at a desired level. Through simulation studies, we demonstrate that the proposed procedure achieves the desired error control, while commonly used alternatives are either overly conservative or fail to adequately control false alarms. Finally, we illustrate the proposed approach using passively collected smartphone data from a cohort of adolescents and young adults with affective instability.
Comments1 table, 2 figures
