We develop a new method for deriving local laws for a large class of random matrices. It is
applicable to many matrix models built from sums and products of deterministic or …

Factor analysis and principal component analysis are used in many application areas. The
first step, choosing the number of components, remains a serious challenge. Our work …

Average-case analysis computes the complexity of an algorithm averaged over all possible
inputs. Compared to worst-case analysis, it is more representative of the typical behavior of …

In this paper, we prove a necessary and sufficient condition for the edge universality of
sample covariance matrices with general population. We consider sample covariance …

For complex Wigner-type matrices, ie Hermitian random matrices with independent, not
necessarily identically distributed entries above the diagonal, we show that at any cusp …

We consider N by N deformed Wigner random matrices of the form XN= HN+ AN, where HN
is a real symmetric or complex Hermitian Wigner matrix and AN is a deterministic real …

In this paper, we are concerned with the deformed Pearcey determinant det⁡(I− γ K s, ρ Pe),
where 0≤ γ< 1 and K s, ρ Pe stands for the trace class operator acting on L 2 (− s, s) with the …

Convergence of eigenvector empirical spectral distribution of sample covariance matrices
Page 1 The Annals of Statistics 2020, Vol. 48, No. 2, 953–982 https://doi.org/10.1214/19-AOS1832 …

The Pearcey kernel is a classical and universal kernel arising from random matrix theory,
which describes the local statistics of eigenvalues when the limiting mean eigenvalue …

This paper focuses on investigating Stein's invariant shrinkage estimators for large sample
covariance matrices and precision matrices in high-dimensional settings. We consider …