时 间：2023 年 10月 30日 09:45-10:45
地 点: 普陀校区理科大楼A1514
Calibration refers to the statistical estimation of parameters in computer experiments, which is often intrinsic and hard to measure with physical tools. This work develops a novel calibration method for imperfect computer models, called Sobolev calibration. The method allows great flexibility for practitioners to achieve both “point-wise value” and “overall shape” approximation, and leads to better physical experiments reconstruction. We prove that the Sobolev calibration enjoys desired theoretical properties including fast convergence rate, asymptotic normality and semiparametric efficiency. We also show that the Sobolev calibration serves as a bridge between two prominent methods: $L_2$ calibration and Kennedy and O'Hagan's calibration method. Additionally, we theoretically justify that our method is applicable to the case when the physical process is indeed a Gaussian process, which follows the original idea of Kennedy and O'Hagan's. Numerical simulations as well as a real-world example illustrate the performance of the proposed method.
王文佳是香港科技大学（广州）信息枢纽数据科学与分析学域的助理教授；2018年8月获得佐治亚理工学院工业工程系博士学位。王文佳的研究方向包括不确定性量化、随机仿真、机器学习、非参数统计和计算机实验。目前已在统计学、机器学习顶级期刊、会议Journal of the American Statistical Association，Journal of Machine Learning Research，Technometrics，Statistica Sinica，NeurIPS，ICLR等发表多篇文章。