Uniform and bootstrap resampling of empirical distributions

Russell R. Barton, Lee W. Schruben

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


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 system 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 interarrivai 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 publicationProceedings of the 25th Conference on Winter Simulation, WSC 1993
EditorsWilliam E. Biles, Gerald W. Evans, Edward C. Russell, Mansooreh Mollaghasemi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)078031381X
StatePublished - Dec 1 1993
Event25th Conference on Winter Simulation, WSC 1993 - Los Angeles, United States
Duration: Dec 12 1993Dec 15 1993

Publication series

NameProceedings - Winter Simulation Conference
VolumePart F129590
ISSN (Print)0891-7736


Other25th Conference on Winter Simulation, WSC 1993
Country/TerritoryUnited States
CityLos Angeles

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


Dive into the research topics of 'Uniform and bootstrap resampling of empirical distributions'. Together they form a unique fingerprint.

Cite this