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Title: Sample Out-of-Sample Inference Based on Wasserstein Distance
We present a novel inference approach that we call sample out-of-sample inference. The approach can be used widely, ranging from semisupervised learning to stress testing, and it is fundamental in the application of data-driven distributionally robust optimization. Our method enables measuring the impact of plausible out-of-sample scenarios in a given performance measure of interest, such as a financial loss. The methodology is inspired by empirical likelihood (EL), but we optimize the empirical Wasserstein distance (instead of the empirical likelihood) induced by observations. From a methodological standpoint, our analysis of the asymptotic behavior of the induced Wasserstein-distance profile function shows dramatic qualitative differences relative to EL. For instance, in contrast to EL, which typically yields chi-squared weak convergence limits, our asymptotic distributions are often not chi-squared. Also, the rates of convergence that we obtain have some dependence on the dimension in a nontrivial way but remain controlled as the dimension increases.  more » « less
Award ID(s):
1820942 1915967
NSF-PAR ID:
10303857
Author(s) / Creator(s):
 ;  
Date Published:
Journal Name:
Operations Research
Volume:
69
Issue:
3
ISSN:
0030-364X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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