EFFICIENT CALIBRATION FOR IMPERFECT COMPUTER MODELS

• 成果类型: Article
• 署名作者: Tuo, Rui; Wu, C. F. Jeff
• 署名单位: Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; University System of Georgia; Georgia Institute of Technology
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/15-AOS1314
• 发表日期: 2015
• 页码: 2331-2352
• 关键词: validation; methodology

摘要

Many computer models contain unknown parameters which need to be estimated using physical observations. Tuo and Wu (2014) show that the calibration method based on Gaussian process models proposed by Kennedy and O'Hagan [J. R. Stat. Soc. Ser. B. Stat. Methodol. 63 (2001) 425-464] may lead to an unreasonable estimate for imperfect computer models. In this work, we extend their study to calibration problems with stochastic physical data. We propose a novel method, called the L-2 calibration, and show its semiparametric efficiency. The conventional method of the ordinary least squares is also studied. Theoretical analysis shows that it is consistent but not efficient. Numerical examples show that the proposed method outperforms the existing ones.