ACM Home Page
Please provide us with feedback. Feedback
Output analysis: output analysis for simulations
Full text PdfPdf (92 KB)
Source Winter Simulation Conference archive
Proceedings of the 32nd conference on Winter simulation table of contents
Orlando, Florida
TUTORIAL SESSION: Advanced tutorials table of contents
Pages: 101 - 108  
Year of Publication: 2000
ISBN:0-7803-6582-8
Authors
Christos Alexopoulos  Georgia Institute of Technology, Atlanta, GA
Andrew F. Seila  University of Georgia, Athens, GA
Sponsors
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/CS : Institute of Electrical and Electronics Engineers/Computer Society
IEEE/SMCS : Institute of Electrical and Electronics Engineers/Systems, Man, and Cybernetics Society
INFORMS-CS : Institute for Operations Research and the Management Sciences-College on Simulation
NIST : National Institute of Standards and Technology
SIGSIM: ACM Special Interest Group on Simulation and Modeling
SCS : The Society for Computer Simulation International
Publisher
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 5,   Citation Count: 3
Additional Information:

abstract   references   cited by   collaborative colleagues  

Tools and Actions: Review this Article  

ABSTRACT

This paper reviews statistical methods for analyzing output data from computer simulations of single systems. In particular, it focuses on the estimation of steady-state system parameters. The estimation techniques include the replication/deletion approach, the regenerative method, the batch means method, and the standardized time series method.


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.

 
1
Alexopoulos, C., and A. F. Seila. 1998. Output data analysis. In Handbook of Simulation, ed. J. Banks, Chapter 7, New York: John Wiley & Sons.
 
2
 
3
Billingsley, P. 1968. Convergence of probability measures, Wiley, New York.
 
4
 
5
Chance, F., and L. W. Schruben. 1992. Establishing a truncation point in simulation output. Technical Report, School of Operations Research and Industrial Engineering, Cornell University, Ithaca, New York.
 
6
Charnes, J. M. 1989. Statistical analysis of multivariate discrete-event simulation output. Ph.D. Thesis, Department of Operations and Management Science, University of Minnesota, Minneapolis, Minnesota.
 
7
 
8
9
 
10
Chien, C.-H. 1989. Small sample theory for steady state confidence intervals. Technical Report No. 37, Department of Operations Research, Stanford University, Palo Alto, California.
 
11
 
12
Chow, Y. S., and H. Robbins. 1965. On the asymptotic theory of fixed-width sequential confidence intervals for the mean. Annals of Mathematical Statistics 36:457-462.
 
13
Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10:47-61.
14
15
 
16
Crane, M. A., and D. L. Iglehart. 1975. Simulating stable stochastic systems III: Regenerative processes and discrete-event simulations. Operations Research 23:33-45.
 
17
 
18
Fishman, G. S. 1973. Statistical analysis for queueing simulations. Management Science 20:363-369.
 
19
Fishman, G. S. 1974. Estimation of multiserver queueing simulations. Operations Research 22:72-78.
 
20
 
21
Fishman, G. S. 1996. Monte Carlo: Concepts, algorithms, and applications. New York: Springer Verlag.
 
22
Fishman, G. S. 2000. Private communication.
 
23
Fishman, G. S., and L. S. Yarberry. 1997. An implementation of the batch means method. INFORMS Journal on Computing 9:296-310.
 
24
Gafarian, A. V., C. J. Ancker, and F. Morisaku. 1978. Evaluation of commonly used rules for detecting steady-state in computer simulation. Naval Research Logistics Quarterly 25:511-529.
 
25
 
26
 
27
Goldsman, D., and L. W. Schruben. 1984. Asymptotic properties of some confidence interval estimators for simulation output. Management Science 30:1217-1225.
 
28
 
29
Goldsman, D., L. W. Schruben, and J. J. Swain. 1994. Tests for transient means in simulated time series. Naval Research Logistics 41:171-187.
 
30
Heidelberger, P., and P. A. W. Lewis. 1984. Quantile estimation in dependent sequences. Operations Research 32:185-209.
31
 
32
Iglehart, D. L. 1975. Simulating stable stochastic systems, V: Comparison of ratio estimators. Naval Research Logistics Quarterly 22:553-565.
33
 
34
Iglehart, D. L. 1978. The regenerative method for simulation analysis. In Current Trends in Programming Methodology, Vol. III, eds. K. M. Chandy, and K. M. Yeh, 52-71. Prentice-Hall, Englewood Cliffs, New Jersey.
 
35
Kelton, W. D. 1989. Random initialization methods in simulation. IIE Transactions 21:355-367.
 
36
Law, A. M., and J. S. Carson. 1979. A sequential procedure for determining the length of a steady-state simulation. Operations Research 27:1011-1025.
 
37
 
38
Mechanic, H., and W. McKay. 1966. Confidence intervals for averages of dependent data in simulations II. Technical Report ASDD 17-202, IBM Corporation, Yorktown Heights, New York.
 
39
 
40
Moore, L. W. 1980. Quantile estimation in regenerative processes. Ph.D. Thesis, Curriculum in Operations Research and Systems Analysis, University of North Carolina, Chapel Hill, North Carolina.
 
41
Nadas, A. 1969. An extension of the theorem of Chow and Robbins on sequential confidence intervals for the mean. Annals of Mathematical Statistics 40:667-671.
 
42
Ockerman, D. H. 1995. Initialization bias tests for stationary stochastic processes based upon standardized time series techniques. Ph.D. Thesis, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
 
43
 
44
 
45
Schmeiser, B. W. 1982. Batch size effects in the analysis of simulation output. Operations Research 30:556-568.
 
46
 
47
Schruben, L. W. 1982. Detecting initialization bias in simulation output. Operations Research 30:569-590.
 
48
Schruben, L. W. 1983. Confidence interval estimation using standardized time series. Operations Research 31:1090-1108.
 
49
Schruben, L. W., H. Singh, and L. Tierney. 1983. Optimal tests for initialization bias in simulation output. Operations Research 31:1167-1178.
 
50
Seila, A. F. 1982a. A batching approach to quantile estimation in regenerative simulations. Management Science 28:573-581.
 
51
Seila, A. F. 1982b. Percentile estimation in discrete event simulation. Simulation 39:193-200.
 
52
 
53
von Neumann, J. 1941a. The mean square difference. Annals of Mathematical Statistics 12:153-162.
 
54
von Neumann, J. 1941b. Distribution of the ratio of the mean square successive difference and the variance. Annals of Mathematical Statistics 12:367-395.
 
55
Welch, P. D. 1983. The statistical analysis of simulation results. In The computer performance modeling handbook, ed. S. Lavenberg, 268-328. New York: Academic Press.
56
 
57
Wilson, J. R., and A. A. B. Pritsker. 1978a. A survey of research on the simulation startup problem. Simulation 31:55-58.
 
58
Wilson, J. R., and A. A. B. Pritsker. 1978b. Evaluation of startup policies in simulation experiments. Simulation 31:79-89.
 
59
Yarberry, L. S. 1993. Incorporating a dynamic batch size selection mechanism in a fixed-sample-size batch means procedure. Ph.D. dissertation, Department of Operations Research, University of North Carolina, Chapel Hill, North Carolina.
 
60
Young, L. C. 1941. On randomness of order sequences. Annals of Mathematical Statistics 12:293-300.

Collaborative Colleagues:
Christos Alexopoulos: colleagues
Andrew F. Seila: colleagues