CIRM - Videos & books Library - Change: detection, estimation, segmentation

Virtualconference

Auteurs : Siegmund, David (Auteur de la Conférence)
CIRM (Editeur )

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Résumé : The maximum score statistic is used to detect and estimate changes in the level, slope, or other local feature of a sequance of observations, and to segment the sequence xhen there appear to be multiple changes. Control of false positive errors when observations are auto-correlated is achieved by using a first order autoregressive model. True changes in level or slope can lead to badly biased estimates of the autoregressive parameter and variance, which can result in a loss of power. Modifications of the natural estimators to deal with this difficulty are partially successful. Applications to temperature time series, atmospheric CO2 levels, COVID-19 incidence, excess deaths, copy number variations, and weather extremes illustrate the general theory.
This is joint research with Xiao Fang.

Keywords : Change point; broken line; segmentation

Codes MSC :
62H10 - Distribution of statistics
62J02 - General nonlinear regression
62L10 - Sequential analysis

Ressources complémentaires :
https://www.cirm-math.fr/RepOrga/2146/Slides/Siegmund_talkluminy20.pdf

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Hennenfent, Guillaume

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https://videos.cirm-math.fr/2020-06-08_Siegmund.mp4

Informations sur la Rencontre

Nom de la rencontre : Mathematical Methods of Modern Statistics 2 / Méthodes mathématiques en statistiques modernes 2
Organisateurs 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.19643303
Citer cette vidéo: Siegmund, David (2020). Change: detection, estimation, segmentation. CIRM. Audiovisual resource. doi:10.24350/CIRM.V.19643303
URI : http://dx.doi.org/10.24350/CIRM.V.19643303

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Bibliographie

FANG, Xiao, LI, Jian, et SIEGMUND, David. Segmentation and estimation of change-point models: false positive control and confidence regions. arXiv preprint arXiv:1608.03032, 2016. - https://arxiv.org/abs/1608.03032

FANG, Xiao et SIEGMUND, David. Detection and Estimation of Local Signals. arXiv preprint arXiv:2004.08159, 2020. - https://arxiv.org/abs/2004.08159

FRYZLEWICZ, Piotr, et al. Wild binary segmentation for multiple change-point detection. The Annals of Statistics, 2014, vol. 42, no 6, p. 2243-2281. - http://dx.doi.org/10.1214/14-AOS1245

OLSHEN, Adam B., VENKATRAMAN, E. S., LUCITO, Robert, et al. Circular binary segmentation for the analysis of array‐based DNA copy number data. Biostatistics, 2004, vol. 5, no 4, p. 557-572. - https://doi.org/10.1093/biostatistics/kxh008

ZHANG, Nancy R., SIEGMUND, David O., JI, Hanlee, et al. Detecting simultaneous changepoints in multiple sequences. Biometrika, 2010, vol. 97, no 3, p. 631-645. - https://dx.doi.org/10.1093%2Fbiomet%2Fasq025

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