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Title: A frequentist approach to computer model calibration

Abstract

The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates of convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.

Authors:
 [1];  [2];  [3]
  1. Iowa State Univ., Ames, IA (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  3. Univ. of California, Davis, CA (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1375159
Report Number(s):
LA-UR-14-29354
Journal ID: ISSN 1369-7412
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of the Royal Statistical Society: Series B (Statistical Methodology)
Additional Journal Information:
Journal Volume: 79; Journal Issue: 2; Journal ID: ISSN 1369-7412
Publisher:
Royal Statistical Society - Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Mathematics; Bootstrap; Inverse problem; Model misspecification; Semiparametric modelling; Surrogate model; Uncertainty analysis

Citation Formats

Wong, Raymond K. W., Storlie, Curtis Byron, and Lee, Thomas C. M. A frequentist approach to computer model calibration. United States: N. p., 2016. Web. doi:10.1111/rssb.12182.
Wong, Raymond K. W., Storlie, Curtis Byron, & Lee, Thomas C. M. A frequentist approach to computer model calibration. United States. doi:10.1111/rssb.12182.
Wong, Raymond K. W., Storlie, Curtis Byron, and Lee, Thomas C. M. Thu . "A frequentist approach to computer model calibration". United States. doi:10.1111/rssb.12182. https://www.osti.gov/servlets/purl/1375159.
@article{osti_1375159,
title = {A frequentist approach to computer model calibration},
author = {Wong, Raymond K. W. and Storlie, Curtis Byron and Lee, Thomas C. M.},
abstractNote = {The paper considers the computer model calibration problem and provides a general frequentist solution. Under the framework proposed, the data model is semiparametric with a non-parametric discrepancy function which accounts for any discrepancy between physical reality and the computer model. In an attempt to solve a fundamentally important (but often ignored) identifiability issue between the computer model parameters and the discrepancy function, the paper proposes a new and identifiable parameterization of the calibration problem. It also develops a two-step procedure for estimating all the relevant quantities under the new parameterization. This estimation procedure is shown to enjoy excellent rates of convergence and can be straightforwardly implemented with existing software. For uncertainty quantification, bootstrapping is adopted to construct confidence regions for the quantities of interest. As a result, the practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.},
doi = {10.1111/rssb.12182},
journal = {Journal of the Royal Statistical Society: Series B (Statistical Methodology)},
issn = {1369-7412},
number = 2,
volume = 79,
place = {United States},
year = {2016},
month = {5}
}

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