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Change: detection, estimation, segmentation

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Virtualconference
Authors : Siegmund, David (Author of the conference)
CIRM (Publisher )

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Abstract : 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

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

Additional resources :
https://www.cirm-math.fr/RepOrga/2146/Slides/Siegmund_talkluminy20.pdf

    Information on the Video

    Film maker : Hennenfent, Guillaume
    Language : English
    Available date : 15/06/2020
    Conference Date : 08/06/2020
    Subseries : Research talks
    arXiv category : Statistics Theory
    Mathematical Area(s) : Probability & Statistics
    Format : MP4 (.mp4) - HD
    Video Time : 00:38:00
    Targeted Audience : Researchers
    Download : https://videos.cirm-math.fr/2020-06-08_Siegmund.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.19643303
Cite this video as: 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

See Also

Bibliography

  • 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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