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Joint estimation of multiple graphical models from high dimensional time series

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  • Huitong Qiu
  • Fang Han
  • Han Liu
  • Brian Caffo

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  • Huitong Qiu & Fang Han & Han Liu & Brian Caffo, 2016. "Joint estimation of multiple graphical models from high dimensional time series," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 487-504, March.
    • Handle: RePEc:bla:jorssb:v:78:y:2016:i:2:p:487-504
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    File URL: http://hdl.handle.net/10.1111/rssb.12123
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    References listed on IDEAS

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    1. Jian Guo & Elizaveta Levina & George Michailidis & Ji Zhu, 2011. "Joint estimation of multiple graphical models," Biometrika, Biometrika Trust, vol. 98(1), pages 1-15.
    2. Suhasini Subba Rao, 2008. "Statistical analysis of a spatio‐temporal model with location‐dependent parameters and a test for spatial stationarity," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(4), pages 673-694, July.
    3. Patrick Danaher & Pei Wang & Daniela M. Witten, 2014. "The joint graphical lasso for inverse covariance estimation across multiple classes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(2), pages 373-397, March.
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    Cited by:

    1. Suprateek Kundu & Benjamin B. Risk, 2021. "Scalable Bayesian matrix normal graphical models for brain functional networks," Biometrics, The International Biometric Society, vol. 77(2), pages 439-450, June.
    2. Chen, Song Xi & Guo, Bin & Qiu, Yumou, 2023. "Testing and signal identification for two-sample high-dimensional covariances via multi-level thresholding," Journal of Econometrics, Elsevier, vol. 235(2), pages 1337-1354.
    3. Chen, Xin & Yang, Dan & Xu, Yan & Xia, Yin & Wang, Dong & Shen, Haipeng, 2023. "Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data," Journal of Econometrics, Elsevier, vol. 232(2), pages 544-564.
    4. Kean Ming Tan & Yang Ning & Daniela M. Witten & Han Liu, 2016. "Replicates in high dimensions, with applications to latent variable graphical models," Biometrika, Biometrika Trust, vol. 103(4), pages 761-777.
    5. Lin Zhang & Andrew DiLernia & Karina Quevedo & Jazmin Camchong & Kelvin Lim & Wei Pan, 2021. "A random covariance model for bi‐level graphical modeling with application to resting‐state fMRI data," Biometrics, The International Biometric Society, vol. 77(4), pages 1385-1396, December.

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