We give an overview of random matrix theory (RMT) with the objective of highlighting the
results and concepts that have a growing impact in the formulation and inference of …

The aim of this book is to investigate the spectral properties of random matrices (RM) when
their dimensions tend to infinity. All classical limiting theorems in statistics are under the …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Random matrix theory has found many applications in physics, statistics and engineering
since its inception. Although early developments were motivated by practical experimental …

We consider the eigenvalues and eigenvectors of finite, low rank perturbations of random
matrices. Specifically, we prove almost sure convergence of the extreme eigenvalues and …

This paper deals with a multivariate Gaussian observation model where the eigenvalues of
the covariance matrix are all one, except for a finite number which are larger. Of interest is …

In this paper, we give a brief review of the theory of spectral analysis of large dimensional
random matrices. Most of the existing work in the literature has been stated for real matrices …

In this paper, we study the least squares (LS) estimator in a linear panel regression model
with unknown number of factors appearing as interactive fixed effects. Assuming that the …

We consider sample covariance matrices S_N= 1 p\Sigma_N^ 1/2 X_NX_N^*\Sigma_N^ 1/2
where XN is a N× p real or complex matrix with iid entries with finite 12th moment and Σ N is …

Let X ij, i, j=…, be a double array of iid complex random variables with EX 11= 0, E| X 11| 2=
1 and E| X 11| 4<∞, and let A_n=1NT_n^1/2X_nX_n^*T_n^1/2, where T n 1/2 is the square …