2026-07-072026-07-07http://salesiana.dossiersoluciones.com/handle/123456789/208381The problem of filtering information from large correlation matrices is of great importance in many applications. We have recently proposed the use of the Kullback-Leibler distance to measure the performance of filtering algorithms in recovering the underlying correlation matrix when the variables are described by a multivariate Gaussian distribution. Here we use the Kullback-Leibler distance to investigate the performance of filtering methods based on Random Matrix Theory and on the shrinkage technique. We also present some results on the application of the Kullback-Leibler distance to multivariate data which are non Gaussian distributed.11 pages, 4 figures, Presented at the Workshop "Random Matrix Theory: From Fundamental Physics To Application", Krakow, Poland, May 3-5, 2007Data Analysis, Statistics and ProbabilityPhysics and SocietyStatistical Financetext