Bootstrap-based Budget Allocation for Nested Simulation

• 成果类型: Article
• 署名作者: Zhang, Kun; Liu, Guangwu; Wang, Shiyu
• 署名单位: Renmin University of China; City University of Hong Kong
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2020.2071
• 发表日期: 2022
• 页码: 1128-1142
• 关键词: risk-estimation

摘要

Simulation budget allocation is at the heart of a nested (also referred to as two level) simulation approach to estimating functionals of a conditional expectation. In this paper, we propose a sample-driven budget allocation rule under a unified nested simulation framework that allows for different forms of functionals. The proposed method employs bootstrap sampling to guide an effective choice of outer-and inner-level sample sizes. Furthermore, we establish a central limit theorem for nested simulation estimators, and incorporate the sample-driven allocation rule into the construction of asymptotically valid confidence intervals (CIs). Effectiveness of the sample-driven allocation rule and validity of the constructed CIs are confirmed by numerical experiments.