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                  <mods:namePart>Gómez-Río, Manuel</mods:namePart>
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                  <mods:namePart>Suckling, John</mods:namePart>
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                  <mods:dateAccessioned encoding="iso8601">2023-11-22T11:47:47Z</mods:dateAccessioned>
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                  <mods:dateIssued encoding="iso8601">2020-09-26</mods:dateIssued>
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               <mods:identifier type="citation">Gorriz, Juan &amp; Group, SiPBA &amp; neuroscience, CAM. (2019). Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities. https://doi.org/10.1016/j.inffus.2020.09.008</mods:identifier>
               <mods:identifier type="uri">https://hdl.handle.net/10630/28106</mods:identifier>
               <mods:identifier type="doi">10.1016/j.inffus.2020.09.008</mods:identifier>
               <mods:abstract>In the 1970s a novel branch of statistics emerged focusing its effort on the selection of a function for the&#xd;
pattern recognition problem that would fulfill a relationship between the quality of the approximation and its&#xd;
complexity. This theory is mainly devoted to problems of estimating dependencies in the case of limited sample&#xd;
sizes, and comprise all the empirical out-of sample generalization approaches; e.g. cross validation (CV). In this&#xd;
paper a data-driven approach based on concentration inequalities is designed for testing competing hypothesis&#xd;
or comparing different models. In this sense we derive a Statistical Agnostic (non-parametric) Mapping (SAM)&#xd;
for neuroimages at voxel or regional levels which is able to: (i) relieve the problem of instability with limited&#xd;
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&#xd;
datasets (containing large and small effects) and random task group analyses to compute empirical familywise&#xd;
error rates, this novel framework resulted in a model validation method for small samples over dimension&#xd;
ratios, and a less-conservative procedure than FWE 𝑝-value correction to determine the significance maps&#xd;
from the inferences made using small upper bounds of the actual risk.</mods:abstract>
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               <mods:subject>
                  <mods:topic>Medicina - Proceso de datos</mods:topic>
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               <mods:subject>
                  <mods:topic>Estadística médica</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Complejidad computacional</mods:topic>
               </mods:subject>
               <mods:subject>
                  <mods:topic>Comprobación de hipótesis (Estadística)</mods:topic>
               </mods:subject>
               <mods:titleInfo>
                  <mods:title>Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities.</mods:title>
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