2026-05-31T04:17:36Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/232612026-02-03T10:55:04Zcom_10630_2254col_10630_37953

Distributionally robust stochastic programs with side information based on trimmings Esteban-Pérez, Adrián Morales-González, Juan Miguel Matemáticas aplicadas We consider stochastic programs conditional on some covariate information, where the only knowledge of the possible relationship between the uncertain parameters and the covariates is reduced to a finite data sample of their joint distribution. By exploiting the close link between the notion of trimmings of a probability measure and the partial mass transportation problem, we construct a data-driven Distributionally Robust Optimization (DRO) framework to hedge the decision against the intrinsic error in the process of inferring conditional information from limited joint data. We show that our approach is computationally as tractable as the standard (without side information) Wasserstein-metric-based DRO and enjoys performance guarantees. Furthermore, our DRO framework can be conveniently used to address data-driven decision-making problems under contaminated samples. Finally, the theoretical results are illustrated using a single-item newsvendor problem and a portfolio allocation problem with side information. 2021-11-23T08:09:06Z 2021-11-23T08:09:06Z 2021-11 journal article https://hdl.handle.net/10630/23261 https://doi.org/10.1007/s10107-021-01724-0 eng Series A; open access Springer