AI 中文总结
研究高维时间序列均值变化检测与定位，采用二次CUSUM统计和坐标方向最大值统计，考虑两种加权方案，在非高斯向量相依性下建立相关理论，证明柯西组合检验合理性，还实现单变化定位和多变化恢复，数值结果验证方法有效性。
AI 中文摘要
本文开发了用于检测和定位高维时间序列中均值变化的自适应程序。二次CUSUM统计针对密集变化，坐标方向最大值统计针对稀疏变化。考虑了两种加权方案以适应内部和边界变化。在一般非高斯向量相依性下，建立了极限分布，验证了所需的中心化和缩放估计量，并证明了匹配的二次和最大值统计之间的渐近独立性。这些结果证明了柯西组合检验的合理性。我们还使用野生二元分割建立了单变化定位和一致的多变化恢复。数值结果说明了所提方法的有效性。
英文摘要
This paper develops adaptive procedures for detecting and locating mean changes in high-dimensional time series. Quadratic CUSUM statistics target dense changes, whereas coordinatewise maximum statistics target sparse changes. Two weighting schemes are considered to accommodate both interior and boundary changes. Under general non-Gaussian vector dependence, we establish the limiting distributions, validate the required centering and scaling estimators, and prove asymptotic independence between matched quadratic and maximum statistics. These results justify Cauchy combination tests. We further establish single-change localization and consistent multiple-change recovery using wild binary segmentation. Numerical results illustrate the effectiveness of the proposed methods.
