Confidence Intervals for Steady-State Simulations: I. A Survey of Fixed Sample Size Procedures

Averill M. Law and W. David Kelton
Additional contact information
Averill M. Law: University of Arizona, Tucson, Arizona
W. David Kelton: The University of Michigan, Ann Arbor, Michigan

Operations Research, 1984, vol. 32, issue 6, 1221-1239

Abstract: We consider the problem of constructing a confidence interval for the steady-state mean of a stochastic process by means of simulation, and study the five main methods which have been proposed (replication, batch means, autoregressive representation, spectrum analysis, and regeneration cycles) for the case when the length of the simulation is fixed in advance. Comparing the performances of these methods on stochastic models with known steady-state means, we find that the simulator should exercise caution in interpreting the results from a simulation of fixed length, and that the length of a simulation adequate for acceptable performance is highly model-dependent. We also investigate possible sources of error for the methods, and conclude that variance estimator bias is more important than point estimator bias in confidence interval coverage degradation.

Keywords: 767; statistical; analysis; of; simulation (search for similar items in EconPapers)
Date: 1984
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.32.6.1221 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:32:y:1984:i:6:p:1221-1239

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().