Optimal Simulator Selection

• 成果类型: Article
• 署名作者: Hung, Ying; Lin, Li-Hsiang; Wu, C. F. Jeff
• 署名单位: Rutgers University System; Rutgers University Newark; Rutgers University New Brunswick; Louisiana State University System; Louisiana State University; University System of Georgia; Georgia Institute of Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1987920
• 发表日期: 2023
• 页码: 1264-1271
• 关键词: computer experiments; MODEL; prediction; diagnostics; calibration

摘要

Computer simulators are widely used for the study of complex systems. In many applications, there are multiple simulators available with different scientific interpretations of the underlying mechanism, and the goal is to identify an optimal simulator based on the observed physical experiments. To achieve the goal, we propose a selection criterion based on leave-one-out cross-validation. This criterion consists of a goodness-of-fit measure and a generalized degrees of freedom penalizing the simulator sensitivity to perturbations in the physical observations. Asymptotic properties of the selected optimal simulator are discussed. It is shown that the proposed procedure includes a conventional calibration method as a special case. The finite sample performance of the proposed procedure is demonstrated through numerical examples. In the application of cell biology, an optimal simulator is selected, which can shed light on the T cell recognition mechanism in the human immune system. Supplementary materials for this article are available online.