Multivariate Standardized Time Series for Steady-State Simulation Output Analysis

David F. Muñoz () and Peter W. Glynn ()
Additional contact information
David F. Muñoz: Departamento de Ingeniería Industrial, Instituto Tecnológico Autónomo de México, Mexico City 01000
Peter W. Glynn: Department of EES & OR, Stanford University, Stanford, California 94305

Operations Research, 2001, vol. 49, issue 3, 413-422

Abstract: The theory of standardized time series, initially proposed to estimate a single steady-state mean from the output of a simulation, is extended to the case where more than one steady-state mean is to be estimated simultaneously. Under mild assumptions on the stochastic process representing the output of the simulation, namely a functional central limit theorem, we obtain asymptotically valid confidence regions for a (multivariate) steady-state mean based on multivariate standardized time series. We provide examples of multivariate standardized time series, including the multivariate versions of the batch means method and Schruben's standardized sum process. Large-sample properties of confidence regions obtained from multivariate standardized time series are discussed. We show that, as in the univariate case, the asymptotic expected volume of confidence regions produced by standardized time series procedures is larger than that obtained from a consistent estimation procedure. We present and discuss experimental results that illustrate our theory.

Keywords: Simulation; statistical analysis: multivariate steady-state output analysis (search for similar items in EconPapers)
Date: 2001
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.49.3.413.11209 (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:49:y:2001:i:3:p:413-422

Access Statistics for this article

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