Revisiting direct bootstrap resampling for input model uncertainty

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations


Metamodel-based bootstrap methods for characterizing input model uncertainty have disadvantages for settings where there are a large number of input distributions, or when using empirical distributions to drive the simulation. Early direct bootstrapping of empirical distributions did not take into account the distinction between intrinsic and extrinsic variations in the resampled quantities. When the intrinsic uncertainty is large, the result is overcoverage of the bootstrap percentile intervals. We explore ways of accounting for both sources in direct bootstrap characterization of input model uncertainty, and study the impact on confidence interval (CI) coverage. Four new bootstrap-based CIs for the expected simulation output under the unknown true distribution are proposed, basic shrinkage CI, percentile shrinkage CI, basic hierarchical bootstrap CI, and percentile hierarchical bootstrap CI, and their empirical performances are demonstrated using an example.

Original languageEnglish (US)
Title of host publicationWSC 2018 - 2018 Winter Simulation Conference
Subtitle of host publicationSimulation for a Noble Cause
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages11
ISBN (Electronic)9781538665725
StatePublished - Jan 31 2019
Event2018 Winter Simulation Conference, WSC 2018 - Gothenburg, Sweden
Duration: Dec 9 2018Dec 12 2018

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


Conference2018 Winter Simulation Conference, WSC 2018

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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