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Title: Confidence regions in Wasserstein distributionally robust estimation
Summary Estimators based on Wasserstein distributionally robust optimization are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing the worst-case loss among all probability models within a certain distance from the underlying empirical measure in a Wasserstein sense. While motivated by the need to identify optimal model parameters or decision choices that are robust to model misspecification, these distributionally robust estimators recover a wide range of regularized estimators, including square-root lasso and support vector machines, among others. This paper studies the asymptotic normality of these distributionally robust estimators as well as the properties of an optimal confidence region induced by the Wasserstein distributionally robust optimization formulation. In addition, key properties of min-max distributionally robust optimization problems are also studied; for example, we show that distributionally robust estimators regularize the loss based on its derivative, and we also derive general sufficient conditions which show the equivalence between the min-max distributionally robust optimization problem and the corresponding max-min formulation.  more » « less
Award ID(s):
1915967 1820942
NSF-PAR ID:
10344973
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Biometrika
Volume:
109
Issue:
2
ISSN:
0006-3444
Page Range / eLocation ID:
295 to 315
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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