Inference in High-Dimensional Online Changepoint Detection
Inference in High-Dimensional Online Changepoint Detection
复制标题
高维在线变点检测中的推理
DOI：
10.1080/01621459.2023.2199962
复制
发表时间：
2023
影响因子：
3.7
通讯作者：
Chen Y
中科院分区：
文献类型：
--
作者：
Chen Y
我们介绍和研究了两个新的推理挑战与高维均值向量的变化的顺序检测。首先，我们寻求一个置信区间的变化点，其次，我们估计的一组指数的坐标中的平均值的变化。我们提出了一种在线算法，产生一个有保证的标称覆盖范围的间隔，其长度是，具有很高的概率，平均检测延迟相同的顺序，对数因子。相应的支持度估计可以控制假阴性和假阳性。模拟证实了我们方法的有效性，我们还说明了其对2017年至2020年美国超额死亡数据的适用性。补充材料，其中包含我们的理论结果的证明，可在线获得。
We introduce and study two new inferential challenges associated with the sequential detection of change in a high-dimensional mean vector. First, we seek a confidence interval for the changepoint, and second, we estimate the set of indices of coordinates in which the mean changes. We propose an online algorithm that produces an interval with guaranteed nominal coverage, and whose length is, with high probability, of the same order as the average detection delay, up to a logarithmic factor. The corresponding support estimate enjoys control of both false negatives and false positives. Simulations confirm the effectiveness of our methodology, and we also illustrate its applicability on the U.S. excess deaths data from 2017 to 2020. The supplementary material, which contains the proofs of our theoretical results, is available online.