Detecting multiple generalized change-points by isolating single ones.
Detecting multiple generalized change-points by isolating single ones.
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DOI：
10.1007/s00184-021-00821-6
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发表时间：
2022
期刊：
影响因子：
0.7
通讯作者：
Fryzlewicz P
中科院分区：
文献类型：
--
作者：
Anastasiou A;Fryzlewicz P
我们介绍了一种新的方法，称为隔离检测（ID），在嘈杂的数据序列中的多个广义变点的数量和位置的一致性估计。ID可以处理的信号变化的例子是分段恒定信号的平均值的变化和线性趋势的连续或非连续变化。变化点的数量可以随着样本量的增加而增加。我们的方法是基于一个隔离技术，它可以防止考虑的间隔，包含一个以上的变点。这种隔离增强了ID的准确性，因为它允许在可能小幅度的频繁变化的存在下进行检测。在ID中，模型选择是通过阈值，或信息准则，或SDLL，或涉及前两者的混合进行的。混合模型选择导致具有非常好的实际性能和最小的参数选择的一般方法。在测试的场景中，ID至少与最先进的方法一样准确;大多数时候它优于它们。ID在R包IDetect和breakfast中实现，可从CRAN获得。在线版补充材料可通过10.1007/s 00184 -021-00821-6获得。
We introduce a new approach, called Isolate-Detect (ID), for the consistent estimation of the number and location of multiple generalized change-points in noisy data sequences. Examples of signal changes that ID can deal with are changes in the mean of a piecewise-constant signal and changes, continuous or not, in the linear trend. The number of change-points can increase with the sample size. Our method is based on an isolation technique, which prevents the consideration of intervals that contain more than one change-point. This isolation enhances ID’s accuracy as it allows for detection in the presence of frequent changes of possibly small magnitudes. In ID, model selection is carried out via thresholding, or an information criterion, or SDLL, or a hybrid involving the former two. The hybrid model selection leads to a general method with very good practical performance and minimal parameter choice. In the scenarios tested, ID is at least as accurate as the state-of-the-art methods; most of the times it outperforms them. ID is implemented in the R packages IDetect and breakfast, available from CRAN. The online version supplementary material available at 10.1007/s00184-021-00821-6.
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影响因子：
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