Model CalibrationWith Censored Data

The purpose of model calibration is to make themodel predictions closer to reality. The classical Kennedy– O’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 app...

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Bibliographic Details
Published in:Technometrics 2018-01, Vol.60 (2), p.255
Main Authors: Cao, Fang, Ba, Shan, Brenneman, William A, Joseph, V Roshan
Format: Article
Language:English
Subjects:
Bayesian analysis
Calibration
Data analysis
Data models
Normal distribution
Parameter estimation
Online Access:Get full text
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Summary:The purpose of model calibration is to make themodel predictions closer to reality. The classical Kennedy– O’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 Kennedy–O’Hagan approach cannot be used directly, and we propose a method to incorporate the censoring information when performingmodel 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. Supplementary materials for this article are available online.
ISSN:0040-1706
1537-2723
DOI:10.1080/00401706.2017.1345704