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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Vydáno v:	Journal of the American Statistical Association Ročník 114; číslo 525; s. 169 - 183
Hlavní autoři:	Fearnhead, Paul, Rigaill, Guillem
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Alexandria Taylor & Francis 02.01.2019 Taylor & Francis Group,LLC Taylor & Francis Ltd
Témata:	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 Penalised likelihood Robust Statistics Biweight loss M-estimation Cusum Binary Segmentation
ISSN:	0162-1459, 1537-274X, 1537-274X
On-line přístup:	Získat plný text
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