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.
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Explicit cutoff profiles for total variation, separation, and other distances are obtained for colored top-m-to-random shuffles, with unused labels converging to Poisson(e^{-c}) at the cutoff time.
citing papers explorer
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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.
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Explicit Cutoff Profiles for Colored Top-$m$-to-Random Shuffles
Explicit cutoff profiles for total variation, separation, and other distances are obtained for colored top-m-to-random shuffles, with unused labels converging to Poisson(e^{-c}) at the cutoff time.