Uniform and bootstrap resampling of empirical distributions

Russell Richard Barton, Lee W. Schruben

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

46 Scopus citations

Abstract

Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities based on the nature of the random variation in the real system. Very often, the probability distributions occurring in the real systems are unknown, and must be estimated using finite samples. This paper presents two ways to estimate simulation model output errors due to the errors in the empirical distributions used to drive the simulation. These approaches are applied to simulations of the M/M/1 queue with an empirically sampled interarrival time. They capture components of variance in the estimate of mean time in the system that are ignored when the empirical distribution is treated as the true distribution.

Original languageEnglish (US)
Title of host publicationWinter Simulation Conference Proceedings
EditorsGerald W. Evans, Mansooren Mollaghasemi, Edward C. Russell, William E. Biles
PublisherPubl by IEEE
Pages503-508
Number of pages6
ISBN (Print)0780313801
StatePublished - Dec 1 1993

Publication series

NameWinter Simulation Conference Proceedings
ISSN (Print)0275-0708

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All Science Journal Classification (ASJC) codes

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

Cite this

Barton, R. R., & Schruben, L. W. (1993). Uniform and bootstrap resampling of empirical distributions. In G. W. Evans, M. Mollaghasemi, E. C. Russell, & W. E. Biles (Eds.), Winter Simulation Conference Proceedings (pp. 503-508). (Winter Simulation Conference Proceedings). Publ by IEEE.