The goal of this paper is to develop methodology for the systematic analysis of asymptotic statistical properties of data driven DRO formulations based on their corresponding non-DRO counterparts. We illustrate our approach in various settings, including both phi-divergence and Wasserstein uncertainty sets. Different types of asymptotic behaviors are obtained depending on the rate at which the uncertainty radius decreases to zero as a function of the sample size and the geometry of the uncertainty sets.

翻译：本文旨在建立方法体系，系统分析基于对应的非分布鲁棒优化模型的数据驱动型分布鲁棒优化（DRO）公式的渐近统计性质。我们在多种场景中阐述了该方法，包括φ-散度和Wasserstein不确定集。根据不确定半径随样本量递减至零的速率以及不确定集的几何结构，我们获得了不同类型的渐近行为。