A frequentist approach to computer model calibration

Raymond K.W. Wong, Curtis B. Storlie, Thomas C.M. Lee

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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. The practical performance of the methodology is illustrated through simulation examples and an application to a computational fluid dynamics model.

Original languageEnglish (US)
Pages (from-to)635-648
Number of pages14
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number2
StatePublished - Mar 1 2017


  • Bootstrap
  • Inverse problem
  • Model misspecification
  • Semiparametric modelling
  • Surrogate model
  • Uncertainty analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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