A Frequentist Approach to Computer Model Calibration
针对计算机模型校准中的不可识别性问题，提出一种新的可识别参数化方法，并开发两步估计程序，通过自助法构建置信区间，适用于物理与计算机模型存在偏差的场景。
Summary The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates of convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. The practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.