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Title: A frequentist approach to computer model calibration

Journal Article · · Journal of the Royal Statistical Society: Series B (Statistical Methodology)
DOI:https://doi.org/10.1111/rssb.12182· OSTI ID:1375159
 [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)
+ Show Author Affiliations

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.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1375159
Report Number(s):
LA-UR-14-29354
Journal Information:
Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 79, Issue 2; ISSN 1369-7412
Publisher:
Royal Statistical Society - WileyCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (2)

Nonparametric estimation of probabilistic sensitivity measures journal August 2019
On the Bayesian calibration of expensive computer models with input dependent parameters text January 2017

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97 MATHEMATICS AND COMPUTING
Mathematics
Bootstrap
Inverse problem
Model misspecification
Semiparametric modelling
Surrogate model
Uncertainty analysis