Open Access
2017 Asymptotic freeness for rectangular random matrices and large deviations for sample covariance matrices with sub-Gaussian tails
Benjamin Groux
Electron. J. Probab. 22: 1-40 (2017). DOI: 10.1214/17-EJP4326

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.

Citation

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Benjamin Groux. "Asymptotic freeness for rectangular random matrices and large deviations for sample covariance matrices with sub-Gaussian tails." Electron. J. Probab. 22 1 - 40, 2017. https://doi.org/10.1214/17-EJP4326

Information

Received: 15 May 2015; Accepted: 9 September 2016; Published: 2017
First available in Project Euclid: 21 June 2017

zbMATH: 1380.60038
MathSciNet: MR3666016
Digital Object Identifier: 10.1214/17-EJP4326

Subjects:
Primary: 46L54 , 60B20 , 60F10

Keywords: Free convolution , information-plus-noise model , large deviations , random matrices , spectral measure , Stieltjes transform , Subordination property

Vol.22 • 2017
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