# Asymptotic independence of point process and Frobenius norm of a large sample covariance matrix

## TL;DR

This paper proves a joint limit theorem for the off-diagonal entries point process and Frobenius norm of a large sample covariance matrix, revealing asymptotic independence and different limiting laws depending on moment conditions.

## Contribution

It establishes the first joint convergence result for dependent point processes and sums in the non-Gaussian setting, extending Kallenberg's theorem.

## Key findings

- Central limit theorem for Frobenius norm with finite fourth moment
- Stable law for Frobenius norm with infinite variance
- Asymptotic independence between point process and Frobenius norm

## Abstract

A joint limit theorem for the point process of the off-diagonal entries of a sample covariance matrix $\mathbf{S}$, constructed from $n$ observations of a $p$-dimensional random vector with iid components, and the Frobenius norm of $\mathbf{S}$ is proved. In particular, assuming that $p$ and $n$ tend to infinity we obtain a central limit theorem for the Frobenius norm in the case of finite fourth moment of the components and an infinite variance stable law in the case of infinite fourth moment. Extending a theorem of Kallenberg, we establish asymptotic independence of the point process and the Frobenius norm of $\mathbf{S}$. To the best of our knowledge, this is the first result about joint convergence of a point process of dependent points and their sum in the non-Gaussian case.