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dc.contributor.author
Cerqueira, Edgardo Daniel
dc.contributor.author
Fraiman Borrazás, Daniel Edmundo
dc.contributor.author
Vargas, Claudia Vanesa
dc.contributor.author
Leonardi, Florencia Graciela
dc.date.available
2019-08-13T19:33:39Z
dc.date.issued
2017-04
dc.identifier.citation
Cerqueira, Edgardo Daniel; Fraiman Borrazás, Daniel Edmundo; Vargas, Claudia Vanesa; Leonardi, Florencia Graciela; A Test of Hypotheses for Random Graph Distributions Built From EEG Data; IEEE Computer Society; IEEE Transactions on Network Science and Engineering; 4; 2; 4-2017; 75-82
dc.identifier.issn
2327-4697
dc.identifier.uri
http://hdl.handle.net/11336/81575
dc.description.abstract
The theory of random graphs has been applied in recent years to model neural interactions in the brain. While the probabilistic properties of random graphs has been extensively studied, the development of statistical inference methods for this class of objects has received less attention. In this work we propose a non-parametric test of hypotheses to test if a sample of random graphs was generated by a given probability distribution (one-sample test) or if two samples of random graphs were originated from the same probability distribution (two-sample test). We prove a Central Limit Theorem providing the asymptotic distribution of the test statistics and we propose a method to compute the quantiles of the finite sample distributions by simulation. The test makes no assumption on the specific form of the distributions and it is consistent against any alternative hypotheses that differs from the sample distribution on at least one edge-marginal. Moreover, we show that the test is a Kolmogorov-Smirnov type test, for a given distance between graphs, and we study its performance on simulated data. We apply it to compare graphs of brain functional network interactions built from electroencephalographic (EEG) data collected during the visualization of point light displays depicting human locomotion.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
IEEE Computer Society
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Eeg
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Kolmogorov-Smirnov Test
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Non-Parametric Test of Hypotheses
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Random Graphs
dc.subject.classification
Astronomía
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Ciencias Físicas
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CIENCIAS NATURALES Y EXACTAS
dc.title
A Test of Hypotheses for Random Graph Distributions Built From EEG Data
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2019-06-06T15:59:04Z
dc.journal.volume
4
dc.journal.number
2
dc.journal.pagination
75-82
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Cerqueira, Edgardo Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidade de Sao Paulo; Brasil
dc.description.fil
Fil: Fraiman Borrazás, Daniel Edmundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de San Andrés. Departamento de Matemáticas y Ciencias; Argentina
dc.description.fil
Fil: Vargas, Claudia Vanesa. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidade Federal Do Rio de Janeiro. Instituto de Biología; Brasil
dc.description.fil
Fil: Leonardi, Florencia Graciela. Universidade de Sao Paulo; Brasil
dc.journal.title
IEEE Transactions on Network Science and Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/7862892/
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/TNSE.2017.2674026
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