WILD BINARY SEGMENTATION FOR MULTIPLE CHANGE-POINT DETECTION
WILD BINARY SEGMENTATION FOR MULTIPLE CHANGE-POINT DETECTION
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DOI：
10.1214/14-aos1245
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发表时间：
2014-12-01
影响因子：
4.5
通讯作者：
Fryzlewicz, Piotr
中科院分区：
文献类型：
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作者：
Fryzlewicz, Piotr
我们提出了一种称为野生二进制分割（WBS）的新技术，用于对数据中多个变化点的数量和位置进行一致估计。我们假设变化点的数量可以随着样本大小而增加到无穷大。由于某种随机定位机制，WBS 即使在变化点之间的间距非常短和/或跳跃幅度非常小的情况下也能工作，这与标准二进制分段不同。另一方面，尽管使用了本地化，WBS 不需要选择窗口或跨度参数，并且不会导致计算复杂性的显着增加。 WBS 也很容易编码。我们提出了 WBS 的两个停止标准：一个基于阈值，另一个基于我们所说的“强化施瓦茨信息标准”。我们提供了该过程参数的默认推荐值，并表明与现有技术相比，它提供了非常好的实用性能。 'WBS 方法在 R 包 wbs 中实现，可在 CRAN 上使用。此外，我们提供了一种新的二进制分段一致性证明，具有改进的收敛速度，以及 WBS 的相应结果。
We propose a new technique, called wild binary segmentation (WBS), for consistent estimation of the number and locations of multiple change-points in data. We assume that the number of change-points can increase to infinity with the sample size. Due to a certain random localisation mechanism, WBS works even for very short spacings between the change-points and/or very small jump magnitudes, unlike standard binary segmentation. On the other hand, despite its use of localisation, WBS does not require the choice of a window or span parameter, and does not lead to a significant increase in computational complexity. WBS is also easy to code. We propose two stopping criteria for WBS: one based on thresholding and the other based on what we term the 'strengthened Schwarz information criterion'. We provide default recommended values of the parameters of the procedure and show that it offers very good practical performance in comparison with the state of the art. The 'WBS methodology is implemented in the R package wbs, available on CRAN.In addition, we provide a new proof of consistency of binary segmentation with improved rates of convergence, as well as a corresponding result for WBS.