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Title: Model Calibration with Censored Data

Abstract

Here, the purpose of model calibration is to make the model 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 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:
 [1];  [2];  [2];  [1]
  1. Georgia Inst. of Technology, Atlanta, GA (United States)
  2. Procter & Gamble Co., Mason, OH (United States)
Publication Date:
Research Org.:
Georgia Tech Research Corp., Atlanta, GA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1405186
Report Number(s):
DOE-GT-0010548-11
Journal ID: ISSN 0040-1706; FG02-13ER26159
Grant/Contract Number:  
SC0010548
Resource Type:
Accepted Manuscript
Journal Name:
Technometrics
Additional Journal Information:
Journal Volume: 60; Journal Issue: 2; Journal ID: ISSN 0040-1706
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. doi:10.1080/00401706.2017.1345704.
Cao, Fang, Ba, Shan, Brenneman, William A., and Joseph, V. Roshan. Wed . "Model Calibration with Censored Data". United States. doi: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 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 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}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Figures / Tables:

Figure 1: Figure 1:: Two samples with pictures taken at initial placement, 120 days and 240 days

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Works referenced in this record:

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Bayesian Calibration of Inexact Computer Models
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journal, October 2017


    Works referencing / citing this record:

    Model Calibration with Censored Data [Supplemental Data]
    dataset, June 2017

    • Cao, Fang; Ba, Shan; Brenneman, William
    • figshare-Supplementary information for journal article at DOI: 10.1080/00401706.2017.1345704, 7 files (193.69 kB)
    • DOI: 10.6084/m9.figshare.5154742

    Tobit models: A survey
    journal, January 1984


    Some results on the multivariate truncated normal distribution
    journal, May 2005


    Bayesian Computation Using Design of Experiments-Based Interpolation Technique
    journal, August 2012


    Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators
    journal, November 2013


    Model Calibration Through Minimal Adjustments
    journal, October 2014


    Engineering-Driven Statistical Adjustment and Calibration
    journal, April 2015


    Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models
    journal, April 2016


    Bayesian Calibration of Inexact Computer Models
    journal, July 2016


    Numerical Computation of Multivariate Normal Probabilities
    journal, June 1992


    On Moments of Folded and Truncated Multivariate Normal Distributions
    journal, October 2017


    Bayesian calibration of computer models
    journal, August 2001

    • Kennedy, Marc C.; O'Hagan, Anthony
    • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 63, Issue 3
    • DOI: 10.1111/1467-9868.00294

    Gaussian predictive process models for large spatial data sets
    journal, September 2008

    • Banerjee, Sudipto; Gelfand, Alan E.; Finley, Andrew O.
    • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 70, Issue 4
    • DOI: 10.1111/j.1467-9868.2008.00663.x

    The normal law under linear restrictions: simulation and estimation via minimax tilting
    journal, February 2016

    • Botev, Z. I.
    • Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 79, Issue 1
    • DOI: 10.1111/rssb.12162

    A Framework for Validation of Computer Models
    journal, May 2007


    Computer Model Calibration Using High-Dimensional Output
    journal, June 2008

    • Higdon, Dave; Gattiker, James; Williams, Brian
    • Journal of the American Statistical Association, Vol. 103, Issue 482
    • DOI: 10.1198/016214507000000888

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