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Estimating whole-brain dynamics by using spectral clustering

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  • Ivor Cribben
  • Yi Yu

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  • Ivor Cribben & Yi Yu, 2017. "Estimating whole-brain dynamics by using spectral clustering," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 607-627, April.
    • Handle: RePEc:bla:jorssc:v:66:y:2017:i:3:p:607-627
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    File URL: http://hdl.handle.net/10.1111/rssc.12169
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    References listed on IDEAS

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    1. Klaus Frick & Axel Munk & Hannes Sieling, 2014. "Multiscale change point inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(3), pages 495-580, June.
    2. Haeran Cho & Piotr Fryzlewicz, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(2), pages 475-507, March.
    3. Claudia Kirch & Birte Muhsal & Hernando Ombao, 2015. "Detection of Changes in Multivariate Time Series With Application to EEG Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1197-1216, September.
    4. Cho, Haeran & Fryzlewicz, Piotr, 2015. "Multiple-change-point detection for high dimensional time series via sparsified binary segmentation," LSE Research Online Documents on Economics 57147, London School of Economics and Political Science, LSE Library.
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    Cited by:

    1. Deborah Sulem & Henry Kenlay & Mihai Cucuringu & Xiaowen Dong, 2022. "Graph similarity learning for change-point detection in dynamic networks," Papers 2203.15470, arXiv.org.

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