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Partial separability and functional graphical models for multivariate Gaussian processes

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Publication:5102497
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DOI10.1093/biomet/asab046OpenAlexW2979709605MaRDI QIDQ5102497

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Publication date: 8 September 2022

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.03134


zbMATH Keywords

separabilityfunctional datainverse covariancefunctional brain connectivity


Mathematics Subject Classification ID

Statistics (62-XX)


Related Items (7)

High-dimensional rank-based graphical models for non-Gaussian functional data ⋮ Linear Manifold Modeling and Graph Estimation based on Multivariate Functional Data with Different Coarseness Scales ⋮ Test of Weak Separability for Spatially Stationary Functional Field ⋮ A Wavelet-Based Independence Test for Functional Data With an Application to MEG Functional Connectivity ⋮ Modeling Massive Highly Multivariate Nonstationary Spatial Data with the Basis Graphical Lasso ⋮ Nonlinear directed acyclic graph estimation based on the kernel partial correlation coefficient ⋮ High-dimensional functional graphical model structure learning via neighborhood selection approach







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