The paper derives a linear shrinkage estimator for mean estimation with auxiliary samples under a Wasserstein-2 constraint, but the claimed exact minimax risk is only an asymptotic-in-N approximation and is contradicted for finite N.
Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations,
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Robust Mean Estimation With Auxiliary Samples
The paper derives a linear shrinkage estimator for mean estimation with auxiliary samples under a Wasserstein-2 constraint, but the claimed exact minimax risk is only an asymptotic-in-N approximation and is contradicted for finite N.