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Virtualconference

Authors : Samworth, Richard (Author of the conference)
CIRM (Publisher )

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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 its worst-case computational complexity per new observation, namely O(p2log(ep)), is 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.

Keywords : Online inference; changepoint detection; multiscale; sparsity

MSC Codes :
62F30 - Inference under constraints
62L10 - Sequential analysis
62L15 - Optimal stopping, See also {60G40, 90D60}

Additional resources :
https://www.cirm-math.fr/RepOrga/2146/Slides/RichardSamworth.pdf

Information on the Video

Film maker :

Hennenfent, Guillaume

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Download :

https://videos.cirm-math.fr/2020-06-04_Samworth.mp4

Information on the Event

Event Title : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2
Event Organizers : Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne
Dates : 15/06/2020 - 19/06/2020
Event Year : 2020
Event URL : https://www.cirm-math.com/cirm-virtual-...

Citation Data

DOI : 10.24350/CIRM.V.19643203
Cite this video as: Samworth, Richard (2020). High-dimensional, multiscale online changepoint detection. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19643203
URI : http://dx.doi.org/10.24350/CIRM.V.19643203

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Bibliography

CHEN, Yudong, WANG, Tengyao, et SAMWORTH, Richard J. High-dimensional, multiscale online changepoint detection. arXiv preprint arXiv:2003.03668, 2020. - https://arxiv.org/abs/2003.03668

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