A Nonparametric Graphical Model for Functional Data With Application to Brain Networks Based on fMRI

We introduce a nonparametric graphical model whose observations on vertices are functions. Many modern applications, such as electroencephalogram and functional magnetic resonance imaging (fMRI), produce data are of this type. The model is based on additive conditional independence (ACI), a statisti...

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Vydáno v:	Journal of the American Statistical Association Ročník 113; číslo 524; s. 1637 - 1655
Hlavní autoři:	Li, Bing, Solea, Eftychia
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Alexandria Taylor & Francis 02.10.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Témata:	Additive conditional independence Additive correlation operator Additive precision operator Apexes Attention deficit hyperactivity disorder Brain Computer simulation Convergence data collection EEG electroencephalography equations fMRI Functional magnetic resonance imaging Functionals Gaussian graphical model Grammatical aspect heteroskedasticity Hilbert space Hyperactivity Independence Iterative methods Kernels Magnetic resonance imaging Mathematical models Matrices Nonparametric statistics Operators (mathematics) Optimization Patients Quantitative analysis Regression analysis Reproducing kernel Hilbert space Simulation Statistical methods Statistics Theory and Methods
ISSN:	0162-1459, 1537-274X, 1537-274X
On-line přístup:	Získat plný text
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