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ABSTRACT
This paper reviews statistical methods for analyzing output data from computer simulations. First, 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. Second, it reviews recent statistical procedures to find the best system among a set of competing alternatives.
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 D. Goldsman. 2002. To batch or not to batch? Technical Report, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
|
| |
2
|
|
| |
3
|
Alexopoulos, C., and A. F. Seila. 1998. Output data analysis. In Handbook of Simulation, ed. J. Banks, Chapter 7. New York: John Wiley & Sons.
|
| |
4
|
Christos Alexopoulos , George S. Fishman , Andrew F. Seila, Computational experience with the batch means method, Proceedings of the 29th conference on Winter simulation, p.194-201, December 07-10, 1997, Atlanta, Georgia, United States
[doi> 10.1145/268437.268477]
|
| |
5
|
Bechhofer, R. E., T. J. Santner, and D. Goldsman. 1995. Design and analysis of experiments for statistical selection, screening and multiple comparisons. New York: John Wiley & Sons.
|
| |
6
|
Billingsley, P. 1968. Convergence of probability measures. New York: John Wiley & Sons.
|
| |
7
|
|
| |
8
|
|
| |
9
|
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.
|
| |
10
|
Charnes, J. M. 1989. Statistical analysis of multivariate discrete-event simulation output. PhD. Thesis, Department of Operations and Management Science, University of Minnesota, Minneapolis, Minnesota.
|
| |
11
|
|
| |
12
|
|
| |
13
|
Hsiao-Chang Chen , Liyi Dai , Chun-Hung Chen , Enver Yücesan, New development of optimal computing budget allocation for discrete event simulation, Proceedings of the 29th conference on Winter simulation, p.334-341, December 07-10, 1997, Atlanta, Georgia, United States
[doi> 10.1145/268437.268501]
|
 |
14
|
|
| |
15
|
|
| |
16
|
|
| |
17
|
|
| |
18
|
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.
|
| |
19
|
|
| |
20
|
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.
|
| |
21
|
Conway, R. W. 1963. Some tactical problems in digital simulation. Management Science 10:47--61.
|
 |
22
|
|
 |
23
|
|
| |
24
|
Crane, M. A., and D. L. Iglehart. 1975. Simulating stable stochastic systems III: Regenerative processes and discrete-event simulations. Operations Research 23:33--45.
|
| |
25
|
|
 |
26
|
|
| |
27
|
Fishman, G. S. 1973. Statistical analysis for queueing simulations. Management Science 20:363--369.
|
| |
28
|
Fishman, G. S. 1974. Estimation of multiserver queueing simulations. Operations Research 22:72--78.
|
| |
29
|
Fishman, G. S. 1996. Monte Carlo: Concepts, algorithms, and applications. New York: Springer Verlag.
|
| |
30
|
|
| |
31
|
Fishman, G. S., and L. S. Yarberry. 1997. An implementation of the batch means method. INFORMS Journal on Computing 9:296--310.
|
| |
32
|
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.
|
| |
33
|
|
| |
34
|
|
| |
35
|
|
| |
36
|
|
| |
37
|
Goldsman, D., and B. L. Nelson. 1998. Comparing systems via simulation. In Handbook of Simulation, ed. J. Banks, Chapter 8. New York: John Wiley & Sons.
|
| |
38
|
Goldsman, D., and L. W. Schruben. 1984. Asymptotic properties of some confidence interval estimators for simulation output. Management Science 30:1217--1225.
|
| |
39
|
|
| |
40
|
Goldsman, D., L. W. Schruben, and J. J. Swain. 1994. Tests for transient means in simulated time series. Naval Research Logistics 41:171--187.
|
| |
41
|
Gupta, S. S. 1965. On some multiple decision (selection and ranking) rules. Technometrics 7:225--245.
|
| |
42
|
Gupta, S. S., and D.-Y. Huang. 1976. Subset selection procedures for the means and variances of normal populations: Unequal sample sizes case, Sankhyā B, 38:112--128.
|
| |
43
|
Heidelberger, P., and P. A. W. Lewis. 1984. Quantile estimation in dependent sequences. Operations Research 32:185--209.
|
| |
44
|
Iglehart, D. L. 1975. Simulating stable stochastic systems, V: Comparison of ratio estimators. Naval Research Logistics Quarterly 22:553--565.
|
 |
45
|
|
| |
46
|
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.
|
 |
47
|
|
| |
48
|
Kelton, W. D. 1989. Random initialization methods in simulation. IIE Transactions 21:355--367.
|
 |
49
|
|
| |
50
|
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.
|
| |
51
|
|
| |
52
|
Malkovich, J. F., and A. A. Afifi. 1973. On tests for multivariate normality. Journal of the American Statistical Association 68:176--179.
|
| |
53
|
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.
|
| |
54
|
|
| |
55
|
Moore, L. W. 1980. Quantile estimation in regenerative processes. PhD. Thesis, Curriculum in Operations Research and Systems Analysis, University of North Carolina, Chapel Hill, North Carolina.
|
| |
56
|
|
| |
57
|
|
| |
58
|
|
| |
59
|
Ockerman, D. H. 1995. Initialization bias tests for stationary stochastic processes based upon standardized time series techniques. PhD. Thesis, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia.
|
| |
60
|
Rinott, Y. 1978. On two-stage selection procedures and related probability-inequalities. Communications in Statistics --- Theory and Methods A 7:799--811.
|
| |
61
|
|
| |
62
|
Schmeiser, B. W. 1982. Batch size effects in the analysis of simulation output. Operations Research 30:556--568.
|
| |
63
|
|
| |
64
|
Schruben, L. W. 1982. Detecting initialization bias in simulation output. Operations Research 30:569--590.
|
| |
65
|
Schruben, L. W. 1983. Confidence interval estimation using standardized time series. Operations Research 31:1090--1108.
|
| |
66
|
Schruben, L. W., H. Singh, and L. Tierney. 1983. Optimal tests for initialization bias in simulation output. Operations Research 31:1167--1178.
|
| |
67
|
Seila, A. F. 1982a. A batching approach to quantile estimation in regenerative simulations. Management Science 28:573--581.
|
| |
68
|
Seila, A. F. 1982b. Percentile estimation in discrete event simulation. Simulation 39:193--200.
|
| |
69
|
|
| |
70
|
|
| |
71
|
Steiger, N. M., and J. R. Wilson. 2002a. An improved batch means procedure for simulation output analysis. Technical Report, Department of Industrial Engineering, North Carolina State University, Raleigh, North Carolina.
|
| |
72
|
Natalie M. Steiger , Christos Alexopoulos , David Goldsman , Emily K. Lada , James R. Wilson , Faker Zouaoui, Output analysis: ASAP2: an improved batch means procedure for simulation output analysis, Proceedings of the 34th conference on Winter simulation: exploring new frontiers, December 08-11, 2002, San Diego, California
|
| |
73
|
von Neumann, J. 1941a. Distribution of the ratio of the mean square successive difference and the variance. Annals of Mathematical Statistics 12:367--395.
|
| |
74
|
von Neumann, J. 1941b. The mean square difference. Annals of Mathematical Statistics 12:153--162.
|
| |
75
|
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.
|
 |
76
|
|
| |
77
|
Wilcox, R. R. 1984. A table for Rinott's selection procedure. Journal of Quality Technology 16:97--100.
|
| |
78
|
Wilson, J. R., and A. A. B. Pritsker. 1978a. A survey of research on the simulation startup problem. Simulation 31:55--58.
|
| |
79
|
Wilson, J. R., and A. A. B. Pritsker. 1978b. Evaluation of startup policies in simulation experiments. Simulation 31:79--89.
|
| |
80
|
Yarberry, L. S. 1993. Incorporating a dynamic batch size selection mechanism in a fixed-sample-size batch means procedure. PhD. dissertation, Department of Operations Research, University of North Carolina, Chapel Hill, North Carolina.
|
|