Robust Calibration of Computer Models Based on Huber Loss

作     者：SUN Yang FANG Xiangzhong SUN Yang;FANG Xiangzhong

作者机构：School of Mathematical SciencesPeking UniversityBeijing 100871China 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2023年第36卷第4期

页      面：1717-1737页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

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

主　　题：Heavy-tailed error M-estimation outliers robustness uncertainty quantification 

摘      要：Recently,uncertainty quantification is getting more and more attention,especially for computer model ***,most of the existing papers assume the errors follow a Gaussian or sub-Gaussian distribution,which would not be satisfied in *** 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 *** 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 *** numerical simulations and a real example are conducted,which verifies good performance of the proposed calibration procedure.