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A General Probabilistic Model for Group Independent Component Analysis and Its Estimation Methods

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  • Ying Guo

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  • Ying Guo, 2011. "A General Probabilistic Model for Group Independent Component Analysis and Its Estimation Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1532-1542, December.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:4:p:1532-1542
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2011.01601.x
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    References listed on IDEAS

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    1. Carl Eckart & Gale Young, 1936. "The approximation of one matrix by another of lower rank," Psychometrika, Springer;The Psychometric Society, vol. 1(3), pages 211-218, September.
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    Cited by:

    1. Joshua Lukemire & Giuseppe Pagnoni & Ying Guo, 2023. "Sparse Bayesian modeling of hierarchical independent component analysis: Reliable estimation of individual differences in brain networks," Biometrics, The International Biometric Society, vol. 79(4), pages 3599-3611, December.
    2. Ben Wu & Subhadip Pal & Jian Kang & Ying Guo, 2022. "Distributional independent component analysis for diverse neuroimaging modalities," Biometrics, The International Biometric Society, vol. 78(3), pages 1092-1105, September.
    3. Zhao, Yuxuan & Matteson, David S. & Mostofsky, Stewart H. & Nebel, Mary Beth & Risk, Benjamin B., 2022. "Group linear non-Gaussian component analysis with applications to neuroimaging," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    4. Benjamin B. Risk & David S. Matteson & David Ruppert & Ani Eloyan & Brian S. Caffo, 2014. "An evaluation of independent component analyses with an application to resting-state fMRI," Biometrics, The International Biometric Society, vol. 70(1), pages 224-236, March.
    5. Ying Guo & Li Tang, 2013. "A Hierarchical Model for Probabilistic Independent Component Analysis of Multi-Subject fMRI Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 970-981, December.

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