Hypothesis testing for network data in functional neuroimaging

DOI10.1214/16-AOAS1015zbMath1391.62217arXiv1407.5525MaRDI QIDQ2404437

Yanyan Li

Publication date: 18 September 2017

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

Full work available at URL: https://arxiv.org/abs/1407.5525

zbMATH Keywords

hypothesis testingnetwork datafMRIFréchet meangraph Laplacianmatrix manifoldobject data

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Parametric hypothesis testing (62F03) Biomedical imaging and signal processing (92C55)

Related Items (24)

Modeling Network Populations via Graph Distances ⋮ Hypothesis Testing for Network Data with Power Enhancement ⋮ Manifold valued data analysis of samples of networks, with applications in corpus linguistics ⋮ Testing for Equivalence of Network Distribution Using Subgraph Counts ⋮ Averages of unlabeled networks: geometric characterization and asymptotic behavior ⋮ Extracting brain disease‐related connectome subgraphs by adaptive dense subgraph discovery ⋮ Hypothesis testing for populations of networks ⋮ A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging ⋮ 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 ⋮ Two-sample and change-point inference for non-Euclidean valued time series ⋮ Modeling sparse longitudinal data on Riemannian manifolds ⋮ Power-Enhanced Simultaneous Test of High-Dimensional Mean Vectors and Covariance Matrices with Application to Gene-Set Testing ⋮ Link prediction in dynamic networks using random dot product graphs ⋮ Unnamed Item ⋮ Fréchet change-point detection ⋮ Network classification with applications to brain connectomics ⋮ Two-sample Hypothesis Testing for Inhomogeneous Random Graphs ⋮ Model-free two-sample test for network-valued data ⋮ Discussion: Object-Oriented Data Analysis, Power Metrics, and Graph Laplacians ⋮ The Fréchet mean of inhomogeneous random graphs ⋮ Unnamed Item ⋮ Sharp local minimax rates for goodness-of-fit testing in multivariate binomial and Poisson families and in multinomials

This page was built for publication: Hypothesis testing for network data in functional neuroimaging