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dc.contributor.authorGórriz-Sáez, Juan Manuel
dc.contributor.authorJiménez-Mesa, Carmen
dc.contributor.authorRomero-García, Raúl
dc.contributor.authorSegovia, Fermín
dc.contributor.authorRamírez, Javier
dc.contributor.authorCastillo-Barnes, Diego
dc.contributor.authorMartínez-Murcia, Francisco Jesús
dc.contributor.authorOrtiz-García, Andrés 
dc.contributor.authorSalas-González, Diego
dc.contributor.authorÁlvarez-Illán, Ignacio
dc.contributor.authorPuntonet, Carlos
dc.contributor.authorLópez-García, Diego
dc.contributor.authorGómez-Río, Manuel
dc.contributor.authorSuckling, John
dc.date.accessioned2023-11-22T11:47:47Z
dc.date.available2023-11-22T11:47:47Z
dc.date.issued2020-09-26
dc.identifier.citationGorriz, Juan & Group, SiPBA & neuroscience, CAM. (2019). Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities. https://doi.org/10.1016/j.inffus.2020.09.008es_ES
dc.identifier.urihttps://hdl.handle.net/10630/28106
dc.description.abstractIn the 1970s a novel branch of statistics emerged focusing its effort on the selection of a function for the pattern recognition problem that would fulfill a relationship between the quality of the approximation and its complexity. This theory is mainly devoted to problems of estimating dependencies in the case of limited sample sizes, and comprise all the empirical out-of sample generalization approaches; e.g. cross validation (CV). In this paper a data-driven approach based on concentration inequalities is designed for testing competing hypothesis or comparing different models. In this sense we derive a Statistical Agnostic (non-parametric) Mapping (SAM) for neuroimages at voxel or regional levels which is able to: (i) relieve the problem of instability with limited sample sizes when estimating the actual risk via CV; and (ii) provide an alternative way of Family-wiseerror (FWE) corrected 𝑝-value maps in inferential statistics for hypothesis testing. Using several neuroimaging datasets (containing large and small effects) and random task group analyses to compute empirical familywise error rates, this novel framework resulted in a model validation method for small samples over dimension ratios, and a less-conservative procedure than FWE 𝑝-value correction to determine the significance maps from the inferences made using small upper bounds of the actual risk.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMedicina - Proceso de datoses_ES
dc.subjectEstadística médicaes_ES
dc.subjectComplejidad computacionales_ES
dc.subjectComprobación de hipótesis (Estadística)es_ES
dc.subject.otherHypothesis testinges_ES
dc.subject.otherUpper boundses_ES
dc.subject.otherActual and empirical riskses_ES
dc.subject.otherFinite class lemmaes_ES
dc.subject.otherRademacher averageses_ES
dc.subject.otherCross-validationes_ES
dc.titleStatistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doi10.1016/j.inffus.2020.09.008
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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