Robust Calibration of Computer Models Based on Huber Loss  

作　　者：SUN Yang FANG Xiangzhong 

机构地区：[1]School of Mathematical Sciences,Peking University,Beijing 100871,China

出　　处：《Journal of Systems Science & Complexity》2023年第4期1717-1737,共21页系统科学与复杂性学报（英文版）

基　　金：This research was supported by the Science Challenge Project under Grant No.TZ2018001.

摘　　要：Recently,uncertainty quantification is getting more and more attention,especially for computer model calibration.However,most of the existing papers assume the errors follow a Gaussian or sub-Gaussian distribution,which would not be satisfied in practice.To overcome the limitation of the traditional calibration procedures,the authors develop a robust calibration procedure based on Huber loss,which can deal with responses with outliers and heavy-tail errors efficiently.The authors propose two different estimators of the calibration parameters based on ordinary least estimator and L_(2)calibration respectively,and investigate the nonasymptotic and asymptotic properties of the proposed estimators under certain conditions.Some numerical simulations and a real example are conducted,which verifies good performance of the proposed calibration procedure.

关 键 词：Heavy-tailed error M-ESTIMATION OUTLIERS ROBUSTNESS uncertainty quantification 

分 类 号：O212.1[理学—概率论与数理统计]