A1016
Title: Sobolev calibration of imperfect computer models
Authors: Wenjia Wang - HKUST (GZ) (China) [presenting]
Abstract: Calibration refers to the statistical estimation of unknown model parameters in computer experiments, such that computer experiments can match underlying physical systems. The aim is to develop a new calibration method for imperfect computer models, Sobolev calibration, which can rule out calibration parameters that generate overfitting calibrated functions. It is proved that the Sobolev calibration enjoys desired theoretical properties, including fast convergence rate, asymptotic normality and semiparametric efficiency. An interesting property is also demonstrated that the Sobolev calibration can bridge the gap between two influential methods: $L_2$ calibration and Kennedy and O'Hagan's calibration. In addition to exploring the deterministic physical experiments, the proposed method is theoretically justified that can transfer to the case when the physical process is indeed a Gaussian process, which follows the original idea of Kennedy and O'Hagan. Numerical simulations, as well as a real-world example, illustrate the competitive performance of the proposed method.
