A computationally efficient, high‐dimensional multiple changepoint procedure with application to global terrorism incidence
A computationally efficient, high‐dimensional multiple changepoint procedure with application to global terrorism incidence
复制标题
一种计算高效、高维多变点程序，适用于全球恐怖主义事件
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
P. Fearnhead
中科院分区：
文献类型：
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作者：
S. Tickle;I. Eckley;P. Fearnhead
在具有许多变量的数据集中检测变点是一个越来越重要的数据科学挑战。从全球恐怖主义数据库中检测恐怖主义发生率变化的问题，我们提出了一种新的方法来检测多变量时间序列中的多个变点。我们的方法，我们称之为SUBSET，是一种基于模型的方法，它使用惩罚似然来检测各种参数设置的变化。我们提供的理论，指导选择的处罚使用SUBSET，并表明它有很高的权力，以检测变化，无论是只有几个变量或许多变量的变化。实证结果表明，SUBSET优于许多现有的方法来检测高斯数据中均值的变化;此外，与这些替代方法不同，它可以很容易地扩展到非高斯设置，例如适用于对恐怖事件计数进行建模。
Detecting changepoints in data sets with many variates is a data science challenge of increasing importance. Motivated by the problem of detecting changes in the incidence of terrorism from a global terrorism database, we propose a novel approach to multiple changepoint detection in multivariate time series. Our method, which we call SUBSET, is a model‐based approach which uses a penalised likelihood to detect changes for a wide class of parametric settings. We provide theory that guides the choice of penalties to use for SUBSET, and that shows it has high power to detect changes regardless of whether only a few variates or many variates change. Empirical results show that SUBSET out‐performs many existing approaches for detecting changes in mean in Gaussian data; additionally, unlike these alternative methods, it can be easily extended to non‐Gaussian settings such as are appropriate for modelling counts of terrorist events.
影响因子：
0.7
作者：
Anastasiou A;Fryzlewicz P
通讯作者：
Fryzlewicz P
影响因子：
2.4
作者：
Tickle S
通讯作者：
Tickle S