# Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures

@article{Singh2020TightBF, title={Tight Bounds for a Class of Data-Driven Distributionally Robust Risk Measures}, author={Derek Singh and Shuzhong Zhang}, journal={arXiv: Optimization and Control}, year={2020} }

This paper expands the notion of robust moment problems to incorporate distributional ambiguity using Wasserstein distance as the ambiguity measure. The classical Chebyshev-Cantelli (zeroth partial moment) inequalities, Scarf and Lo (first partial moment) bounds, and semideviation (second partial moment) in one dimension are investigated. The infinite dimensional primal problems are formulated and the simpler finite dimensional dual problems are derived. A principal motivating question is how… Expand

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