High‐dimensional, multiscale online changepoint detection
Publication Date
2022-02Journal Title
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
ISSN
1369-7412
Publisher
Oxford University Press (OUP)
Language
en
Type
Article
This Version
AO
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Metadata
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Chen, Y., Wang, T., & Samworth, R. J. (2022). High‐dimensional, multiscale online changepoint detection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) https://doi.org/10.1111/rssb.12447
Abstract
Abstract: We introduce a new method for high‐dimensional, online changepoint detection in settings where a p‐variate Gaussian data stream may undergo a change in mean. The procedure works by performing likelihood ratio tests against simple alternatives of different scales in each coordinate, and then aggregating test statistics across scales and coordinates. The algorithm is online in the sense that both its storage requirements and worst‐case computational complexity per new observation are independent of the number of previous observations; in practice, it may even be significantly faster than this. We prove that the patience, or average run length under the null, of our procedure is at least at the desired nominal level, and provide guarantees on its response delay under the alternative that depend on the sparsity of the vector of mean change. Simulations confirm the practical effectiveness of our proposal, which is implemented in the R package ocd, and we also demonstrate its utility on a seismology data set.
Keywords
ORIGINAL ARTICLE, ORIGINAL ARTICLES, average run length, detection delay, high‐dimensional changepoint detection, online algorithm, sequential method
Sponsorship
EPSRC (EP/T02772X/1, EP/P031447/1, EP/N031938/1)
Identifiers
rssb12447
External DOI: https://doi.org/10.1111/rssb.12447
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333286
Rights
Licence:
http://creativecommons.org/licenses/by/4.0/
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