Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

Collection Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2

Organizer(s) Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne

Date(s) 15/06/2020 - 19/06/2020

linked URL https://www.cirm-math.com/cirm-virtual-event-2146.html

00:00:00 / 00:00:00

15 25

High-dimensional, multiscale online changepoint detection

By Richard Samworth

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.

Information about the video

Date of recording 04/06/2020
Date of publication 15/06/2020
Institution CIRM
Licence CC BY NC ND
Language English
Audience Researchers
Director(s) Guillaume Hennenfent
Format MP4

Citation data

DOI 10.24350/CIRM.V.19643203
Cite this video Samworth, Richard (04/06/2020). High-dimensional, multiscale online changepoint detection. CIRM. Audiovisual resource. DOI: 10.24350/CIRM.V.19643203
URL https://dx.doi.org/10.24350/CIRM.V.19643203

Domain(s)

Statistics Theory

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

MSC codes

Document(s)

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

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