4.5 Article

Robust Calibration of Computer Models Based on Huber Loss

Journal

JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY
Volume 36, Issue 4, Pages 1717-1737

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s11424-023-1456-x

Keywords

Heavy-tailed error; M-estimation; outliers; robustness; uncertainty quantification

Ask authors/readers for more resources

Recently, more attention has been given to uncertainty quantification in computer model calibration. However, most existing papers assume errors follow a Gaussian or sub-Gaussian distribution, which is not realistic. To overcome this limitation, the authors propose a robust calibration procedure based on Huber loss that can effectively deal with responses containing outliers and heavy-tail errors. Two different estimators of the calibration parameters are proposed using ordinary least squares and L2 calibration, respectively. Through numerical simulations and a real example, the authors verify the good performance of the proposed calibration procedure.
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 L2 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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available