A test of weak separability for multi-way functional data, with application to brain connectivity studies

DOI10.1093/biomet/asy048zbMath1506.62311arXiv1703.10210OpenAlexW2811486974WikidataQ129339851 ScholiaQ129339851MaRDI QIDQ4562731

Brian Lynch, Kehui Chen

Publication date: 18 December 2018

Published in: Biometrika (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1703.10210

zbMATH Keywords

asymptoticshypothesis testingspatio-temporal datatensor productseparable covariancefunctional principal componentmarginal kernel

Mathematics Subject Classification ID

Functional data analysis (62R10) Applications of statistics to biology and medical sciences; meta analysis (62P10) Hypothesis testing in multivariate analysis (62H15)

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Low-Rank Covariance Function Estimation for Multidimensional Functional Data ⋮ A functional-data approach to the Argo data ⋮ Test of Weak Separability for Spatially Stationary Functional Field ⋮ Random Surface Covariance Estimation by Shifted Partial Tracing ⋮ High-dimensional functional graphical model structure learning via neighborhood selection approach ⋮ A test for separability in covariance operators of random surfaces ⋮ Estimation of partial derivative functionals with application to human mortality data analysis ⋮ Region-referenced spectral power dynamics of EEG signals: a hierarchical modeling approach

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