Computer model calibration with confidence and consistency

Matthew Plumlee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


The paper proposes and examines a calibration method for inexact models. The method produces a confidence set on the parameters that includes the best parameter with a desired probability under any sample size. Additionally, this confidence set is shown to be consistent in that it excludes suboptimal parameters in large sample environments. The method works and the results hold with few assumptions; the ideas are maintained even with discrete input spaces or parameter spaces. Computation of the confidence sets and approximate confidence sets is discussed. The performance is illustrated in a simulation example as well as two real data examples.

Original languageEnglish (US)
Pages (from-to)519-545
Number of pages27
JournalJournal of the Royal Statistical Society. Series B: Statistical Methodology
Issue number3
StatePublished - Jul 2019


  • Frequentist
  • Inverse problem
  • Model discrepancy
  • Model inadequacy
  • Model misspecification
  • Non-linear regression
  • Uncertainty quantification

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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