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White noise assumptions revisited : regression models and statistical designs for simulation practice

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Author Info
Kleijnen, Jack P.C. (Tilburg University, Center for Economic Research)

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Abstract

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

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

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Find related papers by JEL classification:
C0 - Mathematical and Quantitative Methods - - General
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C9 - Mathematical and Quantitative Methods - - Design of Experiments
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Statistical Decision Theory; Operations Research

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  1. 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. [Downloadable!] (restricted)
  2. Russell Davidson & James G. MacKinnon, 2006. "Improving the Reliability of Bootstrap Tests with the Fast Double Bootstrap," Working Papers 1044, Queen's University, Department of Economics. [Downloadable!]
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  3. Kleijnen, J.P.C. & Beers, W.C.M. van, 2003. "Application-driven sequential designs for simulation experiments: kriging metamodeling," Discussion Paper 33, Tilburg University, Center for Economic Research. [Downloadable!]
  4. Kleijnen, J.P.C., 1995. "Sensitivity Analysis and Optimization of System Dynamics Models : Regression Analysis and Statistical Design of Experiments," Discussion Paper 4, Tilburg University, Center for Economic Research. [Downloadable!]
  5. 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. [Downloadable!] (restricted)
  6. Kleijnen, J.P.C., 2009. "Sensitivity Analysis of Simulation Models," Discussion Paper 2009-11, Tilburg University, Center for Economic Research. [Downloadable!]
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