2026-05-28T12:15:21Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/281062026-02-03T11:01:12Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Górriz-Sáez, Juan Manuel author Jiménez-Mesa, Carmen author Romero-García, Raúl author Segovia, Fermín author Ramírez, Javier author Castillo-Barnes, Diego author Martínez-Murcia, Francisco Jesús author Ortiz-García, Andrés author Salas-González, Diego author Álvarez-Illán, Ignacio author Puntonet, Carlos author López-García, Diego author Gómez-Río, Manuel author Suckling, John author 2020-09-26 In 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. Gorriz, Juan & Group, SiPBA & neuroscience, CAM. (2019). Statistical Agnostic Mapping: a Framework in Neuroimaging based on Concentration Inequalities. https://doi.org/10.1016/j.inffus.2020.09.008 https://hdl.handle.net/10630/28106 10.1016/j.inffus.2020.09.008 Medicina - Proceso de datos Estadística médica Complejidad computacional Comprobación de hipótesis (Estadística) Statistical Agnostic Mapping: A framework in neuroimaging based on concentration inequalities.