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Validation of models: statistical techniques and data availability

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Jack P. C. KleijnenAuthors Info & Claims

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1

Pages 647 - 654

https://doi.org/10.1145/324138.324450

Published: 01 December 1999 Publication History

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References

[1]

Beck, M.B., J.R. Ravetz, L.A. Mulkey, and T.O. Barnwell (1997), On the problem of model validation for predictive exposure assessments. Stochastic Hydrology and Hydraulics, 11, pp. 229-254

Crossref

Google Scholar

[2]

Bettonvil, B. and J.P.C. Kleijnen (1997) Searching for important factors in simulation models with many factors: sequential bifurcation. European Journal of Operational Research, 96, no. 1, pp. 180-194

Crossref

Google Scholar

[3]

Conover, W.J. (1971), Practical non-parametric statistics. Wiley, New York

Google Scholar

[4]

Efron, B. and R.J. Tibshirani (1993), Introduction to the bootstrap. Chapman & Hall, London

Google Scholar

[5]

Fossett, C.A., Harrison D., Weintrob H., and Gass S.I. (1991), An assessment procedure for simulation models: a case study, Operations Research 39, pp. 710-723

Digital Library

Google Scholar

[6]

Friedman, L.W. (1996), The simulation metamodel. Kluwer, Dordrecht, Netherlands

Digital Library

Google Scholar

[7]

Halachmi, I et al. (1999), Validation of a simulation model in robotic milking barn design. Working paper, Institute of Agricultural and Environmental Engineering (IMAG-DLO), Wageningen, Netherlands

Google Scholar

[8]

Helton, J.C., D.R. Anderson, M.G. Marietta, and R.P. Rechard (1997), Performance assessment for the waste isolation pilot plant: from regulation to calculation for 40 CFR 191.13. Operations Research, 45, no. 2, pp. 157-177

Digital Library

Google Scholar

[9]

Hodges, J.S. (1991), Six (or so) things you can do with a bad model. Operations Research, 39, no. 3, pp. 355-365

Digital Library

Google Scholar

[10]

Jansen, M. and B. De Vries (1998), Global modelling: managing uncertainty, complexity and incomplete information. Validation of simulation models, eds. C. van Dijkum, D. de Tombe, and E. van Kuijk, SISWO, Amsterdam

Google Scholar

[11]

Johnson N.J. (1978), Modified t tests and confidence intervals for asymmetric populations. Journal of the American Statistical Association, 73, pp. 536-544

Google Scholar

[12]

Kleijnen, J.P.C. (1999), Statistical validation of simulation, including case studies. Validation of simulation models, eds. C. van Dijkum, D. de Tombe, and E. van Kuijk, SISWO, Amsterdam

Google Scholar

[13]

Kleijnen, J.P.C. (1998), Experimental design for sensitivity analysis, optimization, and validation of simulation models. Handbook of simulation, ed. J. Banks, Wiley, New York

Google Scholar

[14]

Kleijnen, J.P.C. (1995a), Case study: statistical validation of simulation models. European Journal of Operational Research, 87, no. 1, pp. 21-34

Crossref

Google Scholar

[15]

Kleijnen, J.P.C. (1995b), Verification and validation of simulation models. European Journal of Operational Research, 82, no. 1, pp. 145-16

Crossref

Google Scholar

[16]

Kleijnen, J.P.C. (1987) Statistical tools for simulation practitioners. Marcel Dekker, New York

Digital Library

Google Scholar

[17]

Kleijnen, J.P.C., B. Bettonvil, and W. Van Groenendaal (1998). Validation of trace-driven simulation models: a novel regression test. Management Science, 44, no. 6, pp. 812-819

Digital Library

Google Scholar

[18]

Kleijnen, J.P.C., B. Bettonvil, and W. Van Groenendaal (1996). Validation of trace-driven simulation models: regression analysis revisited. Proceedings of the 1996 Winter Simulation Conference, eds. J.M. Charnes, D.J. Morrice, D.T. Brunnner, and J.J. Swain, pp. 352-359

Digital Library

Google Scholar

[19]

Kleijnen, J.P.C., R.C.H. Cheng, and B. Bettonvil (1999), Validation of trace-driven simulation models: bootstrapped tests. Working Paper

Google Scholar

[20]

Kleijnen, J.P.C., R.C.H. Cheng, and A.J. Feelders (1998), Bootstrapping and validation of metamodels in simulation. Proceedings of the 1998 Winter Simulation Conference, eds. D.J. Medeiros, E.F. Watson, J.S. Carson, M.S. Manivannan, pp. 701-706

Digital Library

Google Scholar

[21]

Kleijnen, J.P.C. and J. Helton (1999),Statistical analysis of scatter plots to identify important factors in large-scale simulations. Reliability Engineering and Systems Safety, 65, no. 2, pp. 147-1197

Crossref

Google Scholar

[22]

Kleijnen, J.P.C., G.L.J. Kloppenburg, and F.L. Meeuwsen (1986), Testing the mean of an asymmetric population: Johnson's modified t test revisited. Communications in Statistics, Simulation and Computation, 15, no. 3, pp. 715-732

Crossref

Google Scholar

[23]

Kleijnen, J.P.C. and ~. Pala (1999), Maximizing the simulation output: a competition. Simulation (accepted)

Google Scholar

[24]

Kleijnen, J.P.C. and R.G. Sargent (1999), A methodology for the fitting and validation of metamodels in simulation. European Journal of Operational Research (in press)

Google Scholar

[25]

Kleijnen, J.P.C., G. Van Ham, and J. Rotmans (1992), Techniques for sensitivity analysis of simulation models: a case study of the CO2 greenhouse effect. Simulation, 58, no. 6, pp. 410-417

Crossref

Google Scholar

[26]

Knepell, P.L. and D.C. Arangno (1993), Simulation validation; a confidence assessment methodology. IEEE Computer Society Press, Los Alamitos

Digital Library

Google Scholar

[27]

Kozempel, M.F., P. Tomasula, and J.C. Craig (1995), The development of the ERRC food process simulator, Simu-lation; Practice and Theory, 2, 4-5

Google Scholar

[28]

Law A.M., and W.D. Kelton (1991), Simulation modeling and analysis; Second Edition, McGraw-Hill, New York

Digital Library

Google Scholar

[29]

Lysyk, T.J. (1989), Stochastic model of Eastern spruce bud-worm (lepidoptera: tortricidae) phenology on white spruce and balsam fir, Journal of Economic Entomol-ogy, 82, 4, 1161-1168

Crossref

Google Scholar

[30]

Nayani, N. and M. Mollaghasemi (1998), Validation and verification of the simulation model of a photo-lithography process in semiconductor manufacturing. Proceedings of the 1998 Winter Simulation Conference, eds. D.J. Medeiros, E.F. Watson, J.S. Carson, and M.S. Manivannan, pp. 1017-1022

Digital Library

Google Scholar

[31]

Rao, L., L. Owen, and D. Goldsman (1998), Development and application of a validation framework for traffic simulation models. Proceedings of the 1998 Winter Simulation Conference, eds. D.J. Medeiros, E.F. Watson, J.S. Carson, and M.S. Manivannan, pp. 1079- 1086

Digital Library

Google Scholar

[32]

Sargent, R.G. (1996), Verifying and validating simulation models. Proceedings of the 1996 Winter Simulation Conference, eds. J.M. Charnes, D.M. Morrice, D.T. Brunner, and J.J. Swain, pp. 55-64

Digital Library

Google Scholar

[33]

Trybula, W.J. (1994), Building simulation models without data. Proceedings of the International Conference on Systems, Man, and Cybernetics, IEEE, pp. 209-214

Crossref

Google Scholar

[34]

Van der Zouwen, J. and C. van Dijkum (1998), Towards a methodology for the empirical testing of complex social cybernetic models. XIVth ISA World Congress of Sociology

Google Scholar

[35]

Zeigler, B. (1976) Theory of modelling and simulation. Wiley Interscience, New York

Digital Library

Google Scholar

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Index Terms

Validation of models: statistical techniques and data availability
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
2. Mathematics of computing
  1. Probability and statistics

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Published In

WSC '99: Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1

December 1999

925 pages

ISBN:0780357809

DOI:10.1145/324138

Editors:
Phillip A. Farrington
Univ. of Alabama in Huntsville, Huntsville
,
Harriet Black Nembhard
Univ. of Wisconsin—Madison, Madison
,
David T. Sturrock
Systems Modeling, Sewickley, PA
,
Gerald W. Evans
Univ. of Louisville, Louisville, KY

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Published: 01 December 1999

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December 5 - 8, 1999

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WSC '99 Paper Acceptance Rate 139 of 206 submissions, 67%;

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

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