Article

Free access

A replication approach to interval estimation in simulation

Authors:

M. Murat Köksalan,

Nail BasözAuthors Info & Claims

WSC '91: Proceedings of the 23rd conference on Winter simulation

Pages 1023 - 1029

Published: 01 December 1991 Publication History

PDF eReader

References

[1]

Bratley, P., B.L. Fox, and L.E. Schrage. 1987. A Guide to Simulation. New York: Springer-Verlag.

Digital Library

Google Scholar

[2]

Fishman, G.S. 1978. Principles of Discrete Event Simulation. New York: Wiley.

Digital Library

Google Scholar

[3]

Deligonul, Z.S. 1987. Antithetic Bias Reduction for Discrete-Event Simulations. Journal of Operational Research Society 38:431-437.

Crossref

Google Scholar

[4]

Gafarian, A.V., C.J. Ancker, Jr., and T. Morisaku. 1978. Evaluation of Commonly Used Rules for Detecting 'Steady State' in Computer Simulation. Naval Research Logistics Quarterly 25:511-529.

Crossref

Google Scholar

[5]

Kelton, W.D. 1989. Random Initialization Methods in Simulation. lie Transactions 21: 355-367.

Google Scholar

[6]

Kelton, W.D. and A.M. Law. 1983. A New Approach for Dealing with the Startup Problem in Discrete Event Simulation. Naval Research Logistics Quarterly 30:641-658.

Crossref

Google Scholar

[7]

Kelton, W.D. and A.M. Law. 1984. An Analytical Evaluation of Alternative Strategies in Steady-State Simulation. Operations Research 32: 169-184.

Digital Library

Google Scholar

[8]

Kelton, W.D. and A.M. Law. 1985. The Transient Behavior of the M/M/s Queue, with Implications for Steady-State Simulation. Operations Research 33: 378-396.

Digital Library

Google Scholar

[9]

Law, A.M. and W.D. Kelton. 1991. Simulation Modeling and Analysis. New York: McGraw-Hill.

Digital Library

Google Scholar

[10]

Sargent, R.G., K. Kang, and D.M. Goldsman. 1988. An Investigation of Finite Sample Behavior of Confidence Interval Estimation Procedures. Working Paper, Syracuse University, Syracuse, New York.

Google Scholar

[11]

Schruben, L.W. 1982. Detecting Initialization Bias in Simulation Output. Operations Research 30: 569- 590.

Digital Library

Google Scholar

[12]

Schruben, L., H. Singh, and L. Tierney. 1983. Optimal Tests for Initialization Bias in Simulation Output. Operations Research 31:1167-1178.

Digital Library

Google Scholar

[13]

Schmeiser, B. 1990. Simulation Experiments. In Handbooks in OR & MS, v.2: Stochastic Models, eds. D. Heyman and M.J. Sobel, 295-330, Amsterdam: North Holland.

Google Scholar

[14]

Turnquist, M.A. and J.M. Sussman. 1977. Toward Guidelines for Designing Experiments in Queueing Simulation. Simulation 28: 137-144.

Crossref

Google Scholar

[15]

Wilson, J.R. and A.A.B. Pritsker. 1978a. A Survey of Research on the Simulation Startup Problem. Simulation 31: 55-58.

Crossref

Google Scholar

[16]

Wilson, J.R. and A.A.B. Pritsker. 1978b. Evaluation of Startup Policies in Simulation Experiments. Simulation 31: 79-89.

Crossref

Google Scholar

Index Terms

A replication approach to interval estimation in simulation
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
    2. Simulation evaluation
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Queueing theory

Recommendations

Confidence Interval Estimation for the Variance Parameter of Stationary Processes

Asymptotic confidence interval estimators of the variance parameter ý² = lim _{n â ∞} n Var1/ n â ⁿ _{i = 1} X _i are described in this paper for observations X ₁, X ₂,', X _n from a strictly stationary phi-mixing stochastic process. They are based on ...
Confidence Interval Estimation Using Standardized Time Series

Observations of a stationary stochastic process can be transformed into a standardized time series. This paper presents a lemma giving the asymptotic properties of this standardized series under quite general conditions. In particular, the conditions ...
Covariance shaping least-squares estimation

A new linear estimator is proposed, which we refer to as the covariance shaping least-squares (CSLS) estimator, for estimating a set of unknown deterministic parameters, x, observed through a known linear transformation H and corrupted by additive ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

WSC '91: Proceedings of the 23rd conference on Winter simulation

December 1991

1261 pages

ISBN:0780301811

Chairmen:
Barry L. Nelson
The Ohio State Univ., Columbus
,
W. David Kelton
Univ. of Minnesota, Minneapolis
,
Gordon M. Clark
The Ohio State Univ., Columbus

Publisher

IEEE Computer Society

United States

Publication History

Published: 01 December 1991

Check for updates

Qualifiers

Article

Conference

WSC91

Sponsor:

IIE
IEEE-SMCS
ASA
ACM
ORSA
SCS
TIMS
SIGSIM
IEEE-CS
NIST

WSC91: 1991 Winter Simulation Conference

December 8 - 11, 1991

Arizona, Phoenix, USA

Acceptance Rates

Overall Acceptance Rate 3,413 of 5,075 submissions, 67%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

0
Total Citations
130
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)4

Reflects downloads up to 17 Dec 2024

Other Metrics

View Author Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Index Terms

Recommendations

Confidence Interval Estimation for the Variance Parameter of Stationary Processes

Confidence Interval Estimation Using Standardized Time Series

Covariance shaping least-squares estimation