Changepoint Detection in the Presence of Outliers
Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit the outliers. To overcome this problem, data often needs to be preprocessed to remove outliers, though this is diffi...
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| Vydané v: | Journal of the American Statistical Association Ročník 114; číslo 525; s. 169 - 183 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Alexandria
Taylor & Francis
02.01.2019
Taylor & Francis Group,LLC Taylor & Francis Ltd |
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| ISSN: | 0162-1459, 1537-274X, 1537-274X |
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| Abstract | Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit the outliers. To overcome this problem, data often needs to be preprocessed to remove outliers, though this is difficult for applications where the data needs to be analyzed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalized cost approaches for detecting changes so that they use loss functions that are less sensitive to outliers. We argue that loss functions that are bounded, such as the classical biweight loss, are particularly suitable-as we show that only bounded loss functions are robust to arbitrarily extreme outliers. We present an efficient dynamic programming algorithm that can find the optimal segmentation under our penalized cost criteria. Importantly, this algorithm can be used in settings where the data needs to be analyzed online. We show that we can consistently estimate the number of changepoints, and accurately estimate their locations, using the biweight loss function. We demonstrate the usefulness of our approach for applications such as analyzing well-log data, detecting copy number variation, and detecting tampering of wireless devices. Supplementary materials for this article are available online. |
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| AbstractList | Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit the outliers. To overcome this problem, data often needs to be preprocessed to remove outliers, though this is difficult for applications where the data needs to be analyzed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalized cost approaches for detecting changes so that they use loss functions that are less sensitive to outliers. We argue that loss functions that are bounded, such as the classical biweight loss, are particularly suitable-as we show that only bounded loss functions are robust to arbitrarily extreme outliers. We present an efficient dynamic programming algorithm that can find the optimal segmentation under our penalized cost criteria. Importantly, this algorithm can be used in settings where the data needs to be analyzed online. We show that we can consistently estimate the number of changepoints, and accurately estimate their locations, using the biweight loss function. We demonstrate the usefulness of our approach for applications such as analyzing well-log data, detecting copy number variation, and detecting tampering of wireless devices. Supplementary materials for this article are available online. Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints in order to fit the outliers. To overcome this problem, data often needs to be pre-processed to remove outliers, though this is difficult for applications where the data needs to be analysed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalised cost approaches for detecting changes so that they use loss functions that are less sensitive to outliers. We argue that loss functions that are bounded, such as the classical biweight loss, are particularly suitable - as we show that only bounded loss functions are robust to arbitrarily extreme outliers. We present an efficient dynamic programming algorithm that can find the optimal segmentation under our penalised cost criteria. Importantly, this algorithm can be used in settings where the data needs to be analysed online. We show that we can consistently estimate the number of changepoints, and accurately estimate their locations, using the biweight loss function. We demonstrate the usefulness of our approach for applications such as analysing well-log data, detecting copy number variation, and detecting tampering of wireless devices. |
| Author | Fearnhead, Paul Rigaill, Guillem |
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| References | cit0011 Huber P. J. (cit0016) 2011 cit0033 cit0012 cit0034 cit0031 cit0010 cit0032 cit0030 Rigaill G. (cit0036) 2013; 28 Rigaill G. (cit0035) 2015; 156 Hušková M. (cit0020) 2005; 67 cit0019 cit0017 cit0039 Adams R. P. (cit0001) 2007 cit0015 cit0037 Vostrikova L. (cit0038) 1981; 259 cit0013 cit0014 cit0022 cit0023 cit0021 cit0040 cit0041 (cit0018) 2013 cit0008 cit0009 cit0006 cit0028 cit0007 cit0029 cit0004 cit0026 cit0005 cit0027 Yao Y.-C. (cit0042) 1989; 51 cit0002 cit0024 cit0003 cit0025 |
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| SubjectTerms | Algorithms Binary segmentation Biweight loss Change detection copy number variation Cusum Data Data analysis Dynamic programming equations Identification methods Internet Life Sciences M-estimation Needs Noise Outliers (statistics) Penalized likelihood Regression analysis Robust statistics Segmentation Statistical methods Statistics Theory and Methods Usefulness Vegetal Biology Wireless communications |
| Title | Changepoint Detection in the Presence of Outliers |
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