Introduces symmetry-aware convex shrinkage for high-dimensional covariance estimation by selecting a symmetry group via held-out negative log-likelihood and proving regret bounds plus dominance over Ledoit-Wolf under a match condition.
Bickel and Elizaveta Levina
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
A new test statistic and bootstrap for independence testing of high-dimensional nonstationary time series that avoids whitening by removing temporal dependence bias under the null.
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