Technical Note-A New Rate-Optimal Sampling Allocation for Linear Belief Models

• 成果类型: Article
• 署名作者: Zhou, Jiaqi; Ryzhov, Ilya O.
• 署名单位: University System of Maryland; University of Maryland College Park; University System of Maryland; University of Maryland College Park
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.2337
• 发表日期: 2025
• 关键词: simulation budget allocation; regression; optimization; designs

摘要

We derive a new optimal sampling budget allocation for belief models based on linear regression with continuous covariates, where the expected response is interpreted as the value of the covariate vector, and an error occurs if a lower-valued vector is falsely identified as being better than a higher-valued one. Our allocation optimizes the rate at which the probability of error converges to zero using a large deviations theoretic characterization. This is the first large deviations-based optimal allocation for continuous decision spaces, and it turns out to be considerably simpler and easier to implement than allocations that use discretization. We give a practicable sequential implementation and illustrate its empirical potential.