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Title: Statistics of Robust Optimization: A Generalized Empirical Likelihood Approach
We study statistical inference and distributionally robust solution methods for stochastic optimization problems, focusing on confidence intervals for optimal values and solutions that achieve exact coverage asymptotically. We develop a generalized empirical likelihood framework—based on distributional uncertainty sets constructed from nonparametric f-divergence balls—for Hadamard differentiable functionals, and in particular, stochastic optimization problems. As consequences of this theory, we provide a principled method for choosing the size of distributional uncertainty regions to provide one- and two-sided confidence intervals that achieve exact coverage. We also give an asymptotic expansion for our distributionally robust formulation, showing how robustification regularizes problems by their variance. Finally, we show that optimizers of the distributionally robust formulations we study enjoy (essentially) the same consistency properties as those in classical sample average approximations. Our general approach applies to quickly mixing stationary sequences, including geometrically ergodic Harris recurrent Markov chains.  more » « less
Award ID(s):
1934578
NSF-PAR ID:
10382316
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Mathematics of Operations Research
Volume:
46
Issue:
3
ISSN:
0364-765X
Page Range / eLocation ID:
946 to 969
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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