Quantifying input uncertainty via simulation confidence intervals

Russell R. Barton, Barry L. Nelson, Wei Xie

Research output: Contribution to journalArticlepeer-review

90 Scopus citations


We consider the problem of deriving confidence intervals for the mean response of a system that is represented by a stochastic simulation whose parametric input models have been estimated from "real-world" data. As opposed to standard simulation confidence intervals, we provide confidence intervals that account for uncertainty about the input model parameters; our method is appropriate when enough simulation effort can be expended to make simulation-estimation error relatively small. To achieve this we introduce metamodel-assisted bootstrapping that propagates input variability through to the simulation response via an equation-based model rather than by simulating. We develop a metamodel strategy and associated experiment design method that avoid the need for low-order approximation to the response and that minimizes the impact of intrinsic (simulation) error on confidence level accuracy. Asymptotic analysis and empirical tests over a wide range of simulation effort show that confidence intervals obtained via metamodel-assisted bootstrapping achieve the desired coverage.

Original languageEnglish (US)
Pages (from-to)74-87
Number of pages14
JournalINFORMS Journal on Computing
Issue number1
StatePublished - Dec 2014


  • Bootstrapping
  • Confidence intervals
  • Input modeling
  • Metamodeling
  • Stochastic kriging

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Computer Science Applications
  • Management Science and Operations Research


Dive into the research topics of 'Quantifying input uncertainty via simulation confidence intervals'. Together they form a unique fingerprint.

Cite this