Introduces symmetry-aware convex shrinkage estimators for covariance matrices by selecting a symmetry group via held-out predictive performance, generalizing Ledoit-Wolf and group-symmetric MLE with theoretical bounds and real-data 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.
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Symmetry-Aware Convex Shrinkage for High-Dimensional Covariance Estimation
Introduces symmetry-aware convex shrinkage estimators for covariance matrices by selecting a symmetry group via held-out predictive performance, generalizing Ledoit-Wolf and group-symmetric MLE with theoretical bounds and real-data 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.