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Modeling the Spatial and Temporal Dependence in fMRI Data

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  • Gordana Derado
  • F. DuBois Bowman
  • Clinton D. Kilts

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  • Gordana Derado & F. DuBois Bowman & Clinton D. Kilts, 2010. "Modeling the Spatial and Temporal Dependence in fMRI Data," Biometrics, The International Biometric Society, vol. 66(3), pages 949-957, September.
    • Handle: RePEc:bla:biomet:v:66:y:2010:i:3:p:949-957
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2009.01355.x
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    References listed on IDEAS

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    1. Niels Væver Hartvig, 2002. "A Stochastic Geometry Model for Functional Magnetic Resonance Images," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 333-353, September.
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    Cited by:

    1. Soloveychik, I. & Trushin, D., 2016. "Gaussian and robust Kronecker product covariance estimation: Existence and uniqueness," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 92-113.
    2. Stefano Castruccio & Hernando Ombao & Marc G. Genton, 2018. "A scalable multi‐resolution spatio‐temporal model for brain activation and connectivity in fMRI data," Biometrics, The International Biometric Society, vol. 74(3), pages 823-833, September.
    3. Marwan Al-Momani & Abdulkadir A. Hussein & S. E. Ahmed, 2017. "Penalty and related estimation strategies in the spatial error model," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(1), pages 4-30, January.

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