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Computer model calibration with confidence and consistency

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  • Matthew Plumlee

Abstract

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.

Suggested Citation

  • Matthew Plumlee, 2019. "Computer model calibration with confidence and consistency," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(3), pages 519-545, July.
    • Handle: RePEc:bla:jorssb:v:81:y:2019:i:3:p:519-545
    DOI: 10.1111/rssb.12314
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