Asymptotic Freeness for Rectangular Random Matrices and Large Deviations for Sample Covariance Matrices With Sub-Gaussian TailsReportar como inadecuado




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1 LMV - Laboratoire de Mathématiques de Versailles

Abstract : We establish a large deviation principle for the empirical spectral measure of a sample covariance matrix with sub-Gaussian entries, which extends Bordenave and Caputo-s result for Wigner matrices having the same type of entries 7. To this aim, we need to establish an asymptotic freeness result for rectangular free convolution, more precisely, we give a bound in the subordination formula for information-plus-noise matrices.

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Keywords : Information-plus-noise model Random matrices Large deviations Free convolution Subordination property Spectral measure Stieltjes transform

Mots-clés : Grandes déviations Matrices aléatoires Convolution libre Relation de subordination Mesure spectrale Transformée de Stieltjes Modèle information-plus-bruit





Autor: Benjamin Groux -

Fuente: https://hal.archives-ouvertes.fr/



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