The GHOST framework gives near-optimal conditions for universal Gaussian CLTs on linear spectral statistics of sample covariance matrices, with explicit mean-covariance corrections from a bilinear fourth-order kernel and applications to corrected sphericity tests.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.ST 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Resolvents of the sample covariances in the separable mixture model approximate deterministic matrices defined via solutions to a dual system of equations, without simultaneous diagonalizability assumptions.
citing papers explorer
-
The Geometry of Spectral Fluctuations: On Near-Optimal Conditions for Universal Gaussian CLTs, with Statistical Applications
The GHOST framework gives near-optimal conditions for universal Gaussian CLTs on linear spectral statistics of sample covariance matrices, with explicit mean-covariance corrections from a bilinear fourth-order kernel and applications to corrected sphericity tests.
-
Spectral approximation for the separable covariance mixture model
Resolvents of the sample covariances in the separable mixture model approximate deterministic matrices defined via solutions to a dual system of equations, without simultaneous diagonalizability assumptions.