Recovering Best Statistical Guarantees via the Empirical Divergence-Based Distributionally Robust Optimization

• 成果类型: Article
• 署名作者: Lam, Henry
• 署名单位: Columbia University
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2018.1786
• 发表日期: 2019
• 页码: 1090-1105
• 关键词: LIKELIHOOD; approximation; sensitivity; uncertainty; simulation

摘要

We investigate the use of distributionally robust optimization (DRO) as a tractable tool to recover the asymptotic statistical guarantees provided by the central limit theorem, for maintaining the feasibility of an expected value constraint under ambiguous probability distributions. We show that using empirically defined Burg-entropy divergence balls to construct the DRO can attain such guarantees. These balls, however, are not reasoned from the standard data-driven DRO framework because, by themselves, they can have low or even zero probability of covering the true distribution. Rather, their superior statistical performances are endowed by linking the resulting DRO with empirical likelihood and empirical processes. We show that the sizes of these balls can be optimally calibrated using chi(2)-process excursion. We conduct numerical experiments to support our theoretical findings.