A new $$L_2$$ L 2 calibration procedure of computer models based on the smoothing spline ANOVA
Author
Abstract
Suggested Citation
DOI: 10.1007/s00362-023-01478-1
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Gu, Chong, 2014. "Smoothing Spline ANOVA Models: R Package gss," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i05).
- Raymond K. W. Wong & Curtis B. Storlie & Thomas C. M. Lee, 2017. "A frequentist approach to computer model calibration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 635-648, March.
- Drignei, Dorin & Morris, Max D., 2006. "Empirical Bayesian Analysis for Computer Experiments Involving Finite-Difference Codes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1527-1536, December.
- Benedict Götz & Sebastian Kersting & Michael Kohler, 2021. "Estimation of an improved surrogate model in uncertainty quantification by neural networks," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 249-281, April.
- Higdon, Dave & Gattiker, James & Williams, Brian & Rightley, Maria, 2008. "Computer Model Calibration Using High-Dimensional Output," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 570-583, June.
- Matthew Plumlee, 2017. "Bayesian Calibration of Inexact Computer Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1274-1285, July.
- Qiang Sun & Wen-Xin Zhou & Jianqing Fan, 2020. "Adaptive Huber Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 254-265, January.
- Chih-Li Sung & Ying Hung & William Rittase & Cheng Zhu & C. F. J. Wu, 2020. "Calibration for Computer Experiments With Binary Responses and Application to Cell Adhesion Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1664-1674, December.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Sun, Yang & Fang, Xiangzhong, 2024. "Efficient calibration of computer models with multivariate output," Journal of Multivariate Analysis, Elsevier, vol. 202(C).
- Kim, Hyungjin & Park, Chuljin & Kim, Heeyoung, 2025. "Multi-task optimization with Bayesian neural network surrogates for parameter estimation of a simulation model," Computational Statistics & Data Analysis, Elsevier, vol. 204(C).
- SungKu Kang & Ran Jin & Xinwei Deng & Ron S. Kenett, 2023. "Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 415-428, February.
- Maupin, Kathryn A. & Swiler, Laura P., 2020. "Model discrepancy calibration across experimental settings," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
- Huaimin Diao & Yan Wang & Dianpeng Wang, 2022. "A D-Optimal Sequential Calibration Design for Computer Models," Mathematics, MDPI, vol. 10(9), pages 1-15, April.
- Jung, Yongsu & Jo, Hwisang & Choo, Jeonghwan & Lee, Ikjin, 2022. "Statistical model calibration and design optimization under aleatory and epistemic uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
- Wang, Bo & Zhang, Qiong & Xie, Wei, 2019. "Bayesian sequential data collection for stochastic simulation calibration," European Journal of Operational Research, Elsevier, vol. 277(1), pages 300-316.
- Yang, Xuzhi & Wang, Tengyao, 2024. "Multiple-output composite quantile regression through an optimal transport lens," LSE Research Online Documents on Economics 125589, London School of Economics and Political Science, LSE Library.
- Hemez, François M. & Atamturktur, Sezer, 2011. "The dangers of sparse sampling for the quantification of margin and uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(9), pages 1220-1231.
- Drignei, Dorin, 2011. "A general statistical model for computer experiments with time series output," Reliability Engineering and System Safety, Elsevier, vol. 96(4), pages 460-467.
- Hwang, Youngdeok & Kim, Hang J. & Chang, Won & Yeo, Kyongmin & Kim, Yongku, 2019. "Bayesian pollution source identification via an inverse physics model," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 76-92.
- Hugo Maruri-Aguilar & Henry Wynn, 2023. "Sparse polynomial prediction," Statistical Papers, Springer, vol. 64(4), pages 1233-1249, August.
- Yijun Zuo, 2023. "Non-asymptotic analysis and inference for an outlyingness induced winsorized mean," Statistical Papers, Springer, vol. 64(5), pages 1465-1481, October.
- Kleijnen, Jack P.C. & Mehdad, Ehsan, 2014. "Multivariate versus univariate Kriging metamodels for multi-response simulation models," European Journal of Operational Research, Elsevier, vol. 236(2), pages 573-582.
- Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
- L Mark Berliner & Radu Herbei & Christopher K Wikle & Ralph F Milliff, 2023. "Excursions in the Bayesian treatment of model error," PLOS ONE, Public Library of Science, vol. 18(6), pages 1-22, June.
- Xing, Li-Min & Zhang, Yue-Jun, 2022. "Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?," Energy Economics, Elsevier, vol. 110(C).
- Xiaowei Yang & Xinqiao Liu & Haoyu Wei, 2022. "Concentration inequalities of MLE and robust MLE," Papers 2210.09398, arXiv.org, revised Dec 2022.
- Samantha M. Roth & Ben Seiyon Lee & Sanjib Sharma & Iman Hosseini‐Shakib & Klaus Keller & Murali Haran, 2023. "Flood hazard model calibration using multiresolution model output," Environmetrics, John Wiley & Sons, Ltd., vol. 34(2), March.
- Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
More about this item
Keywords
Computer experiments; Imperfect models; Discrepancy function; Uncertainty quantification; Empirical process theory;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stpapr:v:65:y:2024:i:4:d:10.1007_s00362-023-01478-1. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.