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White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice


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  • Kleijnen, J.P.C.

    (Tilburg University, Center for Economic Research)


Classic linear regression models and their concomitant statistical designs assume a univariate response and white noise.By definition, white noise is normally, independently, and identically distributed with zero mean.This survey tries to answer the following questions: (i) How realistic are these classic assumptions in simulation practice?(ii) How can these assumptions be tested? (iii) If assumptions are violated, can the simulation's I/O data be transformed such that the assumptions hold?(iv) If not, which alternative statistical methods can then be applied?

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Bibliographic Info

Paper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2006-50.

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Date of creation: 2006
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Handle: RePEc:dgr:kubcen:200650

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Related research

Keywords: metamodels; experimental designs; generalized least squares; multivariate analysis; normality; jackknife; bootstrap; heteroscedasticity; common random numbers; validation;

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  1. Davidson, Russell & MacKinnon, James G., 2007. "Improving the reliability of bootstrap tests with the fast double bootstrap," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3259-3281, April.
  2. Kleijnen, J.P.C. & Groenendaal, W.J.H. van, 1992. "Simulation: A statistical perspective," Open Access publications from Tilburg University urn:nbn:nl:ui:12-388278, Tilburg University.
  3. Kleijnen, J.P.C., 2009. "Sensitivity Analysis of Simulation Models," Discussion Paper 2009-11, Tilburg University, Center for Economic Research.
  4. Kleijnen, J.P.C. & Groenendaal, W.J.H. van, 1992. "Two-stage versus sequential sample-size determination in regression analysis of simulation experiments," Research Memorandum 572, Tilburg University, Faculty of Economics and Business Administration.
  5. Kleijnen, Jack P.C. & Deflandre, David, 2006. "Validation of regression metamodels in simulation: Bootstrap approach," European Journal of Operational Research, Elsevier, vol. 170(1), pages 120-131, April.
  6. Godfrey, L.G., 2006. "Tests for regression models with heteroskedasticity of unknown form," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2715-2733, June.
  7. Kleijnen, J.P.C., 1995. "Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments," Discussion Paper 1995-4, Tilburg University, Center for Economic Research.
  8. Kleijnen, J.P.C., 1992. "Regression metamodels for simulation with common random numbers: Comparison of validation tests and confidence intervals," Open Access publications from Tilburg University urn:nbn:nl:ui:12-369802, Tilburg University.
  9. Kleijnen, J.P.C., 1993. "Simulation and optimization in production planning: a case study," Open Access publications from Tilburg University urn:nbn:nl:ui:12-369798, Tilburg University.
  10. Kriens, J. & Timmermans , H. & Wildenberg, H. van den & Kleijnen, J.P.C., 1989. "Regression sampling in statistical auditing: A practical survey and evaluation," Open Access publications from Tilburg University urn:nbn:nl:ui:12-365619, Tilburg University.
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Cited by:
  1. Kleijnen, J.P.C., 2006. "Generalized Response Surface Methodology: A New Metaheuristic," Discussion Paper 2006-77, Tilburg University, Center for Economic Research.


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