Network classification with applications to brain connectomics

DOI10.1214/19-AOAS1252zbMath1433.62314arXiv1701.08140WikidataQ104757387 ScholiaQ104757387MaRDI QIDQ2281212

Jesús D. Arroyo Relión, Daniel Kessler, Stephan F. Taylor, Elizaveta Levina

Publication date: 19 December 2019

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1701.08140

zbMATH Keywords

graph topologyhigh-dimensional datavariable selectiongraph classificationfMRI data

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Image analysis in multivariate analysis (62H35) Applications of graph theory (05C90) Biomedical imaging and signal processing (92C55) Neural nets and related approaches to inference from stochastic processes (62M45) Topological data analysis (62R40)

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A Bayesian State-Space Approach to Mapping Directional Brain Networks, Unnamed Item, Averages of unlabeled networks: geometric characterization and asymptotic behavior, Genetic Underpinnings of Brain Structural Connectome for Young Adults, Hypothesis testing for populations of networks, A Spectral-Based Framework for Hypothesis Testing in Populations of Networks, Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome, A practical two-sample test for weighted random graphs, Graph-aware modeling of brain connectivity networks, Vertex nomination, consistent estimation, and adversarial modification, High-dimensional Bayesian network classification with network global-local shrinkage priors, On Whole-Graph Embedding Techniques, Link prediction in dynamic networks using random dot product graphs, Unnamed Item, Network classification with applications to brain connectomics, Scalar on network regression via boosting, Bayesian Regression With Undirected Network Predictors With an Application to Brain Connectome Data, Unnamed Item, Semi-parametric Bayes regression with network-valued covariates

Uses Software

• glmnet
• penalizedSVM
• covTest

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• On asymptotically optimal confidence regions and tests for high-dimensional models
• A nonparametric two-sample hypothesis testing problem for random graphs
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Exact post-selection inference, with application to the Lasso
• A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data
• Minimax rates of community detection in stochastic block models
• Fast first-order methods for composite convex optimization with backtracking
• Smoothing proximal gradient method for general structured sparse regression
• The statistical analysis of fMRI data
• Relaxed Lasso
• Complete mining of frequent patterns from graphs: Mining graph data
• Bayesian inference and testing of group differences in brain networks
• Support-vector networks
• A significance test for the lasso
• Network classification with applications to brain connectomics
• On model selection consistency of regularized M-estimators
• Hypothesis testing for network data in functional neuroimaging
• High-dimensional graphs and variable selection with the Lasso
• Local-aggregate modeling for big data via distributed optimization: Applications to neuroimaging
• A nonparametric view of network models and Newman–Girvan and other modularities
• Stability Selection
• Variable Selection with Error Control: Another Look at Stability Selection
• Learning Theory and Kernel Machines
• Regularization and Variable Selection Via the Elastic Net
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Achieving Optimal Misclassification Proportion in Stochastic Block Model
• Concentration and regularization of random graphs
• Model Selection and Estimation in Regression with Grouped Variables
• Structured sparsity through convex optimization