Ensemble Copula Coupling for Multivariate Input Modeling and Uncertainty Quantification

• 成果类型: Article; Early Access
• 署名作者: Kim, Kyoung-Kuk; Kim, Taeho; Fu, Michael C.
• 署名单位: Korea Advanced Institute of Science & Technology (KAIST); Hong Kong University of Science & Technology; University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
• 刊物名称: MATHEMATICS OF OPERATIONS RESEARCH
• ISSN/ISSBN: 0364-765X; 1526-5471
• DOI: 10.1287/moor.2024.0827
• 发表日期: 2026-07-13
• 关键词: Nonparametric Estimation; input modeling; Uncertainty Quantification; data-driven stochastic modeling; copulas; Empirical Processes; resampling methods; efficient estimation; BAYESIAN FRAMEWORK; distributions; CONVERGENCE; likelihood; RISK

摘要

We consider the problem of estimating a multivariate distribution based on multiple data sources, which include both joint and marginal-only data. Specifically, we introduce a nonparametric approach called Ensemble Copula Coupling (ECC), which can be viewed as a data fusion approach that combines joint and marginal information. The particular setting of interest is input modeling for output analysis of simulated stochastic systems. We apply an ECC-based input model to address uncertainty quantification with correlated inputs. We prove the consistency of ECC and provide limit theorems that can be used to construct confidence intervals for the estimators. Resampling schemes are proposed to improve the accuracy of the confidence intervals. Simulation experiments on queueing and finance examples illustrate the advantages of using the proposed approach and demonstrate that the ECC-based input model can be applied to higher-dimensional problems at lower computational cost compared with existing approaches.