Threshold bootstrap: a new approach to simulation output analysis

Y. B. Kim, T. R. Willemain, J. Haddock, G. C. Runger

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

6 Scopus citations

Abstract

The threshold bootstrap (TB) is a promising new method of inference for a single autocorrelated data series, such as the output of a discrete event simulation. The method works by resampling runs of data created when the series crosses a threshold level, such as the series mean. We performed a Monte Carlo evaluation of the TB using three types of data: white noise, first-order autoregressive, and delays in an M/M/1 queue. The results show that the TB produces accurate and tight estimates of the standard deviation of the sample mean and valid confidence intervals.

Original languageEnglish (US)
Title of host publicationWinter Simulation Conference Proceedings
EditorsGerald W. Evans, Mansooren Mollaghasemi, Edward C. Russell, William E. Biles
PublisherPubl by IEEE
Pages498-502
Number of pages5
ISBN (Print)0780313801
StatePublished - Dec 1 1993
Externally publishedYes

Publication series

NameWinter Simulation Conference Proceedings
ISSN (Print)0275-0708

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Safety, Risk, Reliability and Quality
  • Chemical Health and Safety
  • Applied Mathematics

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