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Sensitivity analysis of simulation experiments : Regression analysis and statistical design

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  • Kleijnen, J.P.C.

    (Tilburg University, Faculty of Economics)

Abstract

This tutorial gives a survey of strategic issues in the statistical design and analysis of experiments with deterministic and random simulation models. These issues concern validation, what-if analysis, optimization, and so on. The analysis uses regression models and least-squares algorithms. The design uses classical experimental designs such as 2k−p factorials, which are more efficient than one at a time designs are. Moreover, classical designs make it possible to estimate interactions among inputs to the simulation. Simulation models may be optimized through response surface methodology, which combines steepest ascent with regression analysis and experimental design. If there are very many inputs, then special techniques such as group screening and sequential bifurcation are useful. Several applications are discussed.
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Kleijnen, J.P.C., 1990. "Sensitivity analysis of simulation experiments : Regression analysis and statistical design," Research Memorandum FEW 440, Tilburg University, School of Economics and Management.
    • Handle: RePEc:tiu:tiurem:25aee5db-38eb-4e05-b032-2f126e4e4b09
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    References listed on IDEAS

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    1. Kleijnen, J.P.C., 1990. "Statistics and deterministic simulation models : Why not?," Research Memorandum FEW 435, Tilburg University, School of Economics and Management.
    2. R. W. Conway, 1963. "Some Tactical Problems in Digital Simulation," Management Science, INFORMS, vol. 10(1), pages 47-61, October.
    3. Bettonvil, B.W.M. & Kleijnen, J.P.C., 1991. "Identifying the important factors in simulation models with many factors," Research Memorandum FEW 498, Tilburg University, School of Economics and Management.
    4. Kleijnen, Jack P. C. & Standridge, Charles R., 1988. "Experimental design and regression analysis in simulation: An FMS case study," European Journal of Operational Research, Elsevier, vol. 33(3), pages 257-261, February.
    5. Kleijnen, J.P.C., 1988. "Simulation and optimization in production planning : A case study (Version 2)," Other publications TiSEM 97a8a024-2229-4f33-8edb-1, Tilburg University, School of Economics and Management.
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    5. Helton, Jon C. & Hansen, Clifford W. & Sallaberry, Cédric J., 2012. "Uncertainty and sensitivity analysis in performance assessment for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 44-63.
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    8. Graham, Tennille & White, Benedict & Pannell, David J., 2003. "Efficiency Policies for Salinity Management: Preliminary Research from a Spatial and Dynamic Metamodel," 2003 Conference (47th), February 12-14, 2003, Fremantle, Australia 57879, Australian Agricultural and Resource Economics Society.
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    15. Helton, J.C. & Johnson, J.D. & Sallaberry, C.J. & Storlie, C.B., 2006. "Survey of sampling-based methods for uncertainty and sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1175-1209.
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    Keywords

    Simulation; mathematische statistiek;

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