Hypothesis Testing of Matrix Graph Model with Application to Brain Connectivity Analysis
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Publication:6079973
DOI10.1111/biom.12633zbMath1522.62263arXiv1511.00718OpenAlexW2963191122WikidataQ39105563 ScholiaQ39105563MaRDI QIDQ6079973
Publication date: 30 October 2023
Published in: Biometrics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1511.00718
multiple testingfalse discovery rateGaussian graphical modelbrain connectivity analysismatrix-variate normal distribution
Related Items (12)
Brain connectivity alteration detection via matrix‐variate differential network model ⋮ Extracting brain disease‐related connectome subgraphs by adaptive dense subgraph discovery ⋮ Nonparametric Functional Graphical Modeling Through Functional Additive Regression Operator ⋮ Simultaneous Cluster Structure Learning and Estimation of Heterogeneous Graphs for Matrix-Variate fMRI Data ⋮ Testing and support recovery of correlation structures for matrix-valued observations with an application to stock market data ⋮ Simultaneous Covariance Inference for Multimodal Integrative Analysis ⋮ Testing the equality of matrix distributions ⋮ Mixed-Effect Time-Varying Network Model and Application in Brain Connectivity Analysis ⋮ Hypothesis Testing for the Covariance Matrix in High-Dimensional Transposable Data with Kronecker Product Dependence Structure ⋮ Network modeling in biology: statistical methods for gene and brain networks ⋮ A Penalized Likelihood Method for Classification With Matrix-Valued Predictors ⋮ Network differential connectivity analysis
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