Multivariate change point detection is the process of identifying distributional shifts in time-ordered data across multiple features. This task is particularly challenging when the number of features is large relative to the number of observations. This problem is often present in mobile health, where behavioral changes in at-risk patients must be detected in real time in order to prompt timely interventions. We propose a variance component score test (VC*) for detecting changes in feature means and/or variances using only pre-change point data to estimate distributional parameters. Through simulation studies, we show that VC* has higher power than existing methods. Moreover, we demonstrate that reducing bias by using only pre-change point days to estimate parameters outweighs the increased estimator variances in most scenarios. Lastly, we apply VC* and competing methods to passively collected smartphone data in adolescents and young adults with affective instability.

翻译：多元变点检测是指识别时间序列数据中多个特征分布变化的过程。当特征数量相对于观测数量较大时，该任务尤为困难。这一问题常见于移动健康领域，需要实时检测高危患者的行为变化以便及时干预。我们提出一种方差分量得分检验（VC*），仅利用变点前数据估计分布参数来检测特征均值和/或方差的变化。通过模拟研究，我们证明VC*比现有方法具有更高的检验功效。此外，我们发现多数情况下仅使用变点前数据估计参数以减少偏差的收益，超过了由此带来的估计量方差增加的影响。最后，我们将VC*与现有方法应用于情感不稳定青少年和年轻成人被动收集的智能手机数据中。