论文标题
顺序更改点检测：计算与统计性能
Sequential change-point detection: Computation versus statistical performance
论文作者
论文摘要
变更点检测研究在更改发生后尽快检测数据流的基础分布的变化的问题。现代的大规模，高维和复杂的流数据呼吁计算（内存）有效的顺序更改点检测算法，这些算法也具有统计上强大的功能。这引起了计算与统计功率权衡，这在过去的经典文献中不太强调的一个方面。本教程采用了这种新的观点，并回顾了几个顺序更改点检测过程，从经典的顺序更改点检测算法到考虑算法设计中计算，记忆效率和模型鲁棒性的最新非参数程序。我们的调查还包含经典的绩效分析，该分析仍然为分析新程序提供了有用的技术。
Change-point detection studies the problem of detecting the changes in the underlying distribution of the data stream as soon as possible after the change happens. Modern large-scale, high-dimensional, and complex streaming data call for computationally (memory) efficient sequential change-point detection algorithms that are also statistically powerful. This gives rise to a computation versus statistical power trade-off, an aspect less emphasized in the past in classic literature. This tutorial takes this new perspective and reviews several sequential change-point detection procedures, ranging from classic sequential change-point detection algorithms to more recent non-parametric procedures that consider computation, memory efficiency, and model robustness in the algorithm design. Our survey also contains classic performance analysis, which still provides useful techniques for analyzing new procedures.
