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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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.
The authors give practical implementations of order-1/2 and order-1 strong schemes for SDDEs with arbitrary fixed delays by combining linear interpolation on a fixed mesh and an augmented variable-step mesh that includes all required delay points.
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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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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.
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Implementation of Milstein Schemes for Stochastic Delay-Differential Equations with Arbitrary Fixed Delays
The authors give practical implementations of order-1/2 and order-1 strong schemes for SDDEs with arbitrary fixed delays by combining linear interpolation on a fixed mesh and an augmented variable-step mesh that includes all required delay points.
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