Model Calibration with Censored Data
Abstract
Here, the purpose of model calibration is to make the model predictions closer to reality. The classical KennedyO'Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the KennedyO'Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied to study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large.
 Authors:

 Georgia Inst. of Technology, Atlanta, GA (United States)
 Procter & Gamble Co., Mason, OH (United States)
 Publication Date:
 Research Org.:
 Georgia Institute of Technology, Atlanta, GA (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
 OSTI Identifier:
 1405186
 Report Number(s):
 DOEGT001054811
Journal ID: ISSN 00401706; FG0213ER26159
 Grant/Contract Number:
 SC0010548
 Resource Type:
 Accepted Manuscript
 Journal Name:
 Technometrics
 Additional Journal Information:
 Journal Volume: 60; Journal Issue: 2; Journal ID: ISSN 00401706
 Publisher:
 Taylor & Francis
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; Bayesian calibration; Computer experiments; Gaussian process; Model discrepancy
Citation Formats
Cao, Fang, Ba, Shan, Brenneman, William A., and Joseph, V. Roshan. Model Calibration with Censored Data. United States: N. p., 2017.
Web. doi:10.1080/00401706.2017.1345704.
Cao, Fang, Ba, Shan, Brenneman, William A., & Joseph, V. Roshan. Model Calibration with Censored Data. United States. https://doi.org/10.1080/00401706.2017.1345704
Cao, Fang, Ba, Shan, Brenneman, William A., and Joseph, V. Roshan. Wed .
"Model Calibration with Censored Data". United States. https://doi.org/10.1080/00401706.2017.1345704. https://www.osti.gov/servlets/purl/1405186.
@article{osti_1405186,
title = {Model Calibration with Censored Data},
author = {Cao, Fang and Ba, Shan and Brenneman, William A. and Joseph, V. Roshan},
abstractNote = {Here, the purpose of model calibration is to make the model predictions closer to reality. The classical KennedyO'Hagan approach is widely used for model calibration, which can account for the inadequacy of the computer model while simultaneously estimating the unknown calibration parameters. In many applications, the phenomenon of censoring occurs when the exact outcome of the physical experiment is not observed, but is only known to fall within a certain region. In such cases, the KennedyO'Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performing model calibration. The method is applied to study the compression phenomenon of liquid inside a bottle. The results show significant improvement over the traditional calibration methods, especially when the number of censored observations is large.},
doi = {10.1080/00401706.2017.1345704},
journal = {Technometrics},
number = 2,
volume = 60,
place = {United States},
year = {2017},
month = {6}
}
Figures / Tables:
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