High-dimensional Gaussian linear processes: Marchenko-Pastur beyond
simultaneous diagonalizability
High-dimensional Gaussian linear processes: Marchenko-Pastur beyond
simultaneous diagonalizability
Except for trivial cases, the eigenvectors of spectral density matrices $f(\theta)$ corresponding to stationary Gaussian process depend explicitly on the frequency $\theta \in [0,2\pi]$. The most commonly used estimator of the spectral density matrix is the smoothed periodogram, which takes the form of sample covariance matrices $YY^T$ for data-matrices $Y$ …