## Concentration of measure and spectra of random matrices: with applications to correlation matrices, elliptical distributions and beyond (2009)

Venue: | THE ANNALS OF APPLIED PROBABILITY TO APPEAR |

Citations: | 8 - 5 self |

### BibTeX

@MISC{Karoui09concentrationof,

author = {Noureddine El Karoui},

title = {Concentration of measure and spectra of random matrices: with applications to correlation matrices, elliptical distributions and beyond},

year = {2009}

}

### OpenURL

### Abstract

We place ourselves in the setting of high-dimensional statistical inference, where the number of variables p in a dataset of interest is of the same order of magnitude as the number of observations n. More formally we study the asymptotic properties of correlation and covariance matrices under the setting that p/n → ρ ∈ (0, ∞), for general population covariance. We show that spectral properties for large dimensional correlation matrices are similar to those of large dimensional covariance matrices, for a large class of models studied in random matrix theory. We also derive a Marčenko-Pastur type system of equations for the limiting spectral distribution of covariance matrices computed from data with elliptical distributions and generalizations of this family. The motivation for this study comes partly from the possible relevance of such distributional assumptions to problems in econometrics and portfolio optimization, as well as robustness questions for certain classical random matrix results. A mathematical theme of the paper is the important use we make of concentration inequalities.