Shrinkage and spectral filtering of correlation matrices: a comparison via the Kullback-Leibler distance

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dc.creatorTumminello, M.
dc.creatorLillo, F.
dc.creatorMantegna, R. N.
dc.date2007-10-02
dc.date.accessioned2026-07-07T12:05:27Z
dc.date.available2026-07-07T12:05:27Z
dc.descriptionThe 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.
dc.description11 pages, 4 figures, Presented at the Workshop "Random Matrix Theory: From Fundamental Physics To Application", Krakow, Poland, May 3-5, 2007
dc.identifierhttps://arxiv.org/abs/0710.0576
dc.identifierhttp://arxiv.org/abs/0710.0576
dc.identifierActa Phys. Pol. B 38 (13), 4079-4088 (2007)
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/208381
dc.subjectData Analysis, Statistics and Probability
dc.subjectPhysics and Society
dc.subjectStatistical Finance
dc.titleShrinkage and spectral filtering of correlation matrices: a comparison via the Kullback-Leibler distance
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