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 US excess deaths data from 2017--2020.

翻译：我们引入并研究了与高维均值向量序列变化检测相关的两个新推断挑战。第一，我们寻求变化点的置信区间；第二，我们估计均值发生变化的坐标索引集合。我们提出一种在线算法，该算法生成的区间具有保证的名义覆盖率，且其长度在高概率下（至多相差一个对数因子）与平均检测延迟同阶。相应的支撑估计同时实现了对假阴性率和假阳性率的控制。模拟实验验证了我们方法的有效性，我们也将其应用于2017-2020年美国超额死亡数据来展示其实用性。