The 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
|Number of pages||10|
|Journal||Acta Physica Polonica B|
|Publication status||Published - 2007|
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)
Mantegna, R. N., Tumminello, M., Lillo, F., Lillo, F., Mantegna, R. N., & Tumminello, M. (2007). Shrinkage and spectral filtering of correlation matrices: a comparison via the Kullback-Leibler distance. Acta Physica Polonica B, 38, 4079-4088.