The threshold bootstrap: A new approach to simulation output analysis

Y. B. Kim, T. R. Willemain, J. Haddock, George Runger

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

Abstract

The threshold bootstrap (TB) is a promising new method of inference for a single autocorrelatcd 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 publicationProceedings of the 25th Conference on Winter Simulation, WSC 1993
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages498-502
Number of pages5
VolumePart F129590
ISBN (Electronic)078031381X
DOIs
StatePublished - Dec 1 1993
Externally publishedYes
Event25th Conference on Winter Simulation, WSC 1993 - Los Angeles, United States
Duration: Dec 12 1993Dec 15 1993

Other

Other25th Conference on Winter Simulation, WSC 1993
CountryUnited States
CityLos Angeles
Period12/12/9312/15/93

ASJC Scopus subject areas

  • Software
  • Modeling and Simulation
  • Computer Science Applications

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