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Epidemic changepoint detection in the presence of nuisance changes
Statistical Papers ( IF 1.3 ) Pub Date : 2022-04-04 , DOI: 10.1007/s00362-022-01307-x
Julius Juodakis 1 , Stephen Marsland 1
Affiliation  

Many time series problems feature epidemic changes—segments where a parameter deviates from a background baseline. Detection of such changepoints can be improved by accounting for the epidemic structure, but this is currently difficult if the background level is unknown. Furthermore, in practical data the background often undergoes nuisance changes, which interfere with standard estimation techniques and appear as false alarms. To solve these issues, we develop a new, efficient approach to simultaneously detect epidemic changes and estimate unknown, but fixed, background level, based on a penalised cost. Using it, we build a two-level detector that models and separates nuisance and signal changes. The analytic and computational properties of the proposed methods are established, including consistency and convergence. We demonstrate via simulations that our two-level detector provides accurate estimation of changepoints under a nuisance process, while other state-of-the-art detectors fail. In real-world genomic and demographic datasets, the proposed method identified and localised target events while separating out seasonal variations and experimental artefacts.



中文翻译:

存在滋扰变化时的流行病变化点检测

许多时间序列问题都具有流行性变化——参数偏离背景基线的部分。可以通过考虑流行病结构来改进对此类变化点的检测,但如果背景水平未知,目前很难做到这一点。此外,在实际数据中,背景经常会发生令人讨厌的变化,这会干扰标准估计技术并表现为误报。为了解决这些问题,我们开发了一种新的、有效的方法来同时检测流行病变化并根据惩罚成本估计未知但固定的背景水平。使用它,我们构建了一个两级检测器,可以对干扰和信号变化进行建模和分离。建立了所提出方法的分析和计算特性,包括一致性和收敛性。我们通过模拟证明,我们的两级检测器在滋扰过程中提供了对变化点的准确估计,而其他最先进的检测器则失败了。在现实世界的基因组和人口统计数据集中,所提出的方法可以识别和定位目标事件,同时分离出季节性变化和实验人工制品。

更新日期:2022-04-04
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