A new $$L_2$$ L 2 calibration procedure of computer models based on the smoothing spline ANOVA

Yang Sun () and Xiangzhong Fang
Additional contact information
Yang Sun: Peking University
Xiangzhong Fang: Peking University

Statistical Papers, 2024, vol. 65, issue 4, No 1, 1926 pages

Abstract: Abstract Computer models cannot always fit the physical systems well in practice. Most of these models contain unknown parameters with high uncertainty. Calibration is an approach to identify these unknown parameters in computer models. Inspired by Tuo and Wu (Ann Stat 43(6):2331–2352, 2015), we propose a new calibration procedure based on the smoothing spline ANOVA, which can be regarded as an extension of the methods of Tuo and Wu (Ann Stat 43(6):2331–2352, 2015). The proposed procedure mainly comprises two steps: computing the improved $$L_2$$ L 2 estimator of the calibration parameters, and estimating the discrepancy function. We derive the rate of convergence of the proposed estimator of the calibration parameters and investigate its asymptotic properties. The proposed method combines the advantages of $$L_2$$ L 2 calibration and ordinary least square (OLS) calibration, and exhibits better performances than Tuo and Wu (Ann Stat 43(6):2331–2352, 2015) and Wong et al. (J R Stat Soc Ser B 79(2):635–645, 2017). We conduct some numerical simulations and apply the proposed method to two real examples, which demonstrate the advantages of the proposed procedure.

Keywords: Computer experiments; Imperfect models; Discrepancy function; Uncertainty quantification; Empirical process theory (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-023-01478-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:65:y:2024:i:4:d:10.1007_s00362-023-01478-1

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-023-01478-1

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().