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Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes

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  • Kang, Jian
  • Johnson, Timothy D.
  • Nichols, Thomas E.
  • Wager, Tor D.

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Suggested Citation

  • Kang, Jian & Johnson, Timothy D. & Nichols, Thomas E. & Wager, Tor D., 2011. "Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 124-134.
    • Handle: RePEc:bes:jnlasa:v:106:i:493:y:2011:p:124-134
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    File URL: http://pubs.amstat.org/doi/abs/10.1198/jasa.2011.ap09735
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    Cited by:

    1. Cholaquidis, Alejandro & Forzani, Liliana & Llop, Pamela & Moreno, Leonardo, 2017. "On the classification problem for Poisson point processes," Journal of Multivariate Analysis, Elsevier, vol. 153(C), pages 1-15.
    2. Wenqiong Xue & Jian Kang & F. DuBois Bowman & Tor D. Wager & Jian Guo, 2014. "Identifying functional co-activation patterns in neuroimaging studies via poisson graphical models," Biometrics, The International Biometric Society, vol. 70(4), pages 812-822, December.
    3. Silvia Montagna & Tor Wager & Lisa Feldman Barrett & Timothy D. Johnson & Thomas E. Nichols, 2018. "Spatial Bayesian latent factor regression modeling of coordinate†based meta†analysis data," Biometrics, The International Biometric Society, vol. 74(1), pages 342-353, March.
    4. Guanzhou Wei & Xiao Liu & Russell Barton, 2024. "An extended PDE‐based statistical spatio‐temporal model that suppresses the Gibbs phenomenon," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.

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