Confidence regions in Wasserstein distributionally robust estimation
نویسندگان
چکیده
Summary Estimators based on Wasserstein distributionally robust optimization are obtained as solutions of min-max problems in which the statistician selects a parameter minimizing worst-case loss among all probability models within certain distance from underlying empirical measure sense. While motivated by need to identify optimal model parameters or decision choices that misspecification, these estimators recover wide range regularized estimators, including square-root lasso and support vector machines, others. This paper studies asymptotic normality well properties an confidence region induced formulation. In addition, key also studied; for example, we show regularize its derivative, derive general sufficient conditions equivalence between problem corresponding max-min
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ژورنال
عنوان ژورنال: Biometrika
سال: 2021
ISSN: ['0006-3444', '1464-3510']
DOI: https://doi.org/10.1093/biomet/asab026