We extend to the matrix setting a recent result of Srivastava-Vershynin about estimating the covariance matrix of a random vector. The result can be in- terpreted as a quantified version of the law of large numbers for positive semi-definite matrices which verify some regularity assumption. Beside giving examples, we dis- cuss the notion of log-concave matrices and give estimates on the smallest and largest eigenvalues of a sum of such matrices
International audienceWe establish new tail estimates for order statistics and for the Euclidean nor...
Let {Xij}, i, j = · · · , be a double array of independent and identically distributed (i.i.d.) real...
165 pagesInternational audienceThis review covers recent results concerning the estimation of large ...
This article studies the limiting behavior of a class of robust population covariance matrix estimat...
This paper deals with the problem of estimating the covariance matrix of a series of independent mul...
International audienceThis paper studies the limiting behavior of a class of robust population covar...
The present work provides an original framework for random matrix analysis based on revisiting the c...
International audienceWe consider n × n real symmetric and hermitian random matrices Hn,m equals the...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a ...
We consider n × n real symmetric and hermitian random matrices Hn,m equals the sum of a non-random m...
This paper demonstrates an introduction to the statistical distribution of eigenval-ues in Random Ma...
This work is concerned with finite range bounds on the variance of individual eigenvalues of random ...
This work introduces the minimax Laplace transform method, a modification of the cumulant-based matr...
Abstract. We study the universality of the eigenvalue statistics of the covariance matrices
An improved estimator of certain bilinear forms of the logarithm of the covariance matrix is present...
International audienceWe establish new tail estimates for order statistics and for the Euclidean nor...
Let {Xij}, i, j = · · · , be a double array of independent and identically distributed (i.i.d.) real...
165 pagesInternational audienceThis review covers recent results concerning the estimation of large ...
This article studies the limiting behavior of a class of robust population covariance matrix estimat...
This paper deals with the problem of estimating the covariance matrix of a series of independent mul...
International audienceThis paper studies the limiting behavior of a class of robust population covar...
The present work provides an original framework for random matrix analysis based on revisiting the c...
International audienceWe consider n × n real symmetric and hermitian random matrices Hn,m equals the...
During the last twenty years, Random matrix theory (RMT) has produced numerous results that allow a ...
We consider n × n real symmetric and hermitian random matrices Hn,m equals the sum of a non-random m...
This paper demonstrates an introduction to the statistical distribution of eigenval-ues in Random Ma...
This work is concerned with finite range bounds on the variance of individual eigenvalues of random ...
This work introduces the minimax Laplace transform method, a modification of the cumulant-based matr...
Abstract. We study the universality of the eigenvalue statistics of the covariance matrices
An improved estimator of certain bilinear forms of the logarithm of the covariance matrix is present...
International audienceWe establish new tail estimates for order statistics and for the Euclidean nor...
Let {Xij}, i, j = · · · , be a double array of independent and identically distributed (i.i.d.) real...
165 pagesInternational audienceThis review covers recent results concerning the estimation of large ...