E. Cabana Garceran del Vall, R. E. Lillo, H. Laniado
This work deals with the functional Magnetic Resonance Imaging (fMRI) statistical analysis. The classical approach based on GLM is not a robust methodology, a very important fact with this kind of data that presents several acquisition artifacts and signal noises. In order to gain in robustness, while maintaining an efficient computational time, we propose a method based on the detection and removal of multivariate outliers in the data, using a robust Mahalanobis distance. This measure is defined in terms of robust estimates of location and scatter based on the notion of “shrinkages”, a very well-known concept in financial statistics and portfolio optimization, that has not been applied to the neuroimaging field before. We present some good preliminary results with simulated fMRI data within a comparison with other existing methodologies in the statistical literature.
Palabras clave: robust regression, multivariate outlier detection, fMRI statistical analysis
Programado
L06.1 Grupo de Análisis Multivariante y Clasificación II
5 de septiembre de 2016 12:55
0.02 - Aula de proyectos 1
