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Folded standardized time series area variance estimators for simulation
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Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come table of contents
Washington D.C.
SESSION: Analysis methodology A: advances in simulation output analysis table of contents
Pages 455-462  
Year of Publication: 2007
ISBN:1-4244-1306-0
Authors
Claudia Antonini  Universidad Simón Bolivar, Sartenejas, Venezuela
Christos Alexopoulos  Georgia Institute of Technology, Atlanta, GA
David Goldsman  Georgia Institute of Technology, Atlanta, GA
James R. Wilson  North Carolina State University, Raleigh, NC
Sponsors
INFORMS-SIM : Institute for Operations Research and the Management Sciences: Simulation Society
NIST : National Institute of Standards and Technology
(SCS) : The Society for Modeling and Simulation International
ACM/SIGSIM : Association for Computing Machinery: Special Interest Group on Simulation
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/SMC : Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
Publisher
IEEE Press  Piscataway, NJ, USA
Bibliometrics
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ABSTRACT

We estimate the variance parameter of a stationary simulation-generated process using "folded" versions of standardized time series area estimators. We formulate improved variance estimators based on the combination of multiple folding levels as well as the use of batching. The improved estimators preserve the asymptotic bias properties of their predecessors but have substantially lower variance. A Monte Carlo example demonstrates the efficacy of the new methodology.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Aktaran-Kalayci, T., D. Goldsman, and J. R. Wilson. 2007. Linear combinations of overlapping variance estimators for simulation. Operations Research Letters 35:439--447.
 
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Alexopoulos, C., C. Antonini, D. Goldsman, M. Meterelliyoz, and J. R. Wilson. 2007a. Properties of folded standardized time series variance estimators for simulation. Technical report, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
 
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Alexopoulos, C., N. T. Argon, D. Goldsman, N. M. Steiger, G. Tokol, and J. R. Wilson. 2007b. Efficient computation of overlapping variance estimators for simulation. INFORMS Journal on Computing 19 (3): 314--327.
 
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Alexopoulos, C., N. T. Argon, D. Goldsman, G. Tokol, and J. R. Wilson. 2007c. Overlapping variance estimators for simulation. Operations Research to appear. Available online via <ftp.ncsu.edu/pub/eos/pub/jwilson/ovestv72.pdf> {accessed June 17, 2007}.
 
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Antonini, C. 2005. Folded variance estimators for stationary time series. Ph.D. thesis, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia. Available online via <hd1.handle.net/1853/6931> {accessed June 17, 2007}.
 
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Antonini, C., C. Alexopoulos, D. Goldsman, and J. R. Wilson. 2007. Area variance estimators for simulation using folded standardized time series. Technical report, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
 
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Billingsley, P. 1968. Convergence of probability measures. New York: Wiley.
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Goldsman, D., K. Kang, S. H. Kim, A. F. Seila, and G. Tokol. 2007. Combining standardized time series area and Cramér-von Mises variance estimators. Naval Research Logistics 54:384--396.
 
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Schruben, L. 1983. Confidence interval estimation using standardized time series. Operations Research 31:1090--1108.
 
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Shorack, G. R., and J. A. Wellner. 1986. Empirical processes with applications to statistics. New York: Wiley.
 
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Collaborative Colleagues:
Claudia Antonini: colleagues
Christos Alexopoulos: colleagues
David Goldsman: colleagues
James R. Wilson: colleagues