LSD of sample covariances of superposition of matrices with separable covariance structure

We study the asymptotic behavior of the spectra of matrices of the form $S_n = \frac{1}{n}XX^*$ where $X =\sum_{r=1}^K X_r$, where $X_r = A_r^\frac{1}{2}Z_rB_r^\frac{1}{2}$, $K \in \mathbb{N}$ and $A_r,B_r$ are sequences of positive semi-definite matrices of dimensions $p\times p$ and $n\times n$, respectively. We establish the existence of a limiting spectral distribution for $S_n$ by assuming that matrices ${A_r}{r=1}^K$ are simultaneously diagonalizable and ${B_r}{r=1}^K$ are simultaneously digaonalizable, and that the joint spectral distributions of ${A_r}{r=1}^K$ and ${B_r}{r=1}^K$ converge to $K$-dimensional distributions, as $p,n\to \infty$ such that $p/n \to c \in (0,\infty)$. The LSD of $S_n$ is characterized by system of equations with unique solutions within the class of Stieltjes transforms of measures on $\mathbb{R}_+$. These results generalize existing results on the LSD of sample covariances when the data matrices have a separable covariance structure.

💡 Research Summary

The paper investigates the asymptotic spectral behavior of sample covariance matrices built from a sum of several independent “separable‑covariance” components. Specifically, the data matrix is modeled as

\