English
High-dimensional, multiscale online changepoint detection
Virtualconference
Auteurs : Samworth, Richard (Auteur de la Conférence)
CIRM (Editeur )
62F30 - Inference under constraints
62L10 - Sequential analysis
62L15 - Optimal stopping, See also {60G40, 90D60}
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/RichardSamworth.pdf
CIRM (Editeur )
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Résumé :
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
Codes MSC : 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
62F30 - Inference under constraints
62L10 - Sequential analysis
62L15 - Optimal stopping, See also {60G40, 90D60}
Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/RichardSamworth.pdf
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Informations sur la Vidéo
Réalisateur : Hennenfent, GuillaumeLangue : Anglais Date de publication : 15/06/2020 Date de captation : 04/06/2020 Sous collection : Research talks arXiv category : Statistics Theory Domaine : Probability & Statistics Format : MP4 (.mp4) - HD Durée : 00:39:43 Audience : Researchers Download : https://videos.cirm-math.fr/2020-06-04_Samworth.mp4 
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Informations sur la Rencontre
Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2Organisateurs de la rencontre : Bogdan, Malgorzata ; Graczyk, Piotr ; Panloup, Fabien ; Proïa, Frédéric ; Roquain, Etienne Dates : 15/06/2020 - 19/06/2020 Année de la rencontre : 2020 URL Congrès : https://www.cirm-math.com/cirm-virtual-...
Données de citation
DOI : 10.24350/CIRM.V.19643203Citer cette vidéo: 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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Bibliographie
- 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
