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Simulation Experiments in Practice: Statistical Design and Regression Analysis

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

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Abstract

In practice, simulation analysts often change only one factor at a time, and use graphical analysis of the resulting Input/Output (I/O) data. Statistical theory proves that more information is obtained when applying Design Of Experiments (DOE) and linear regression analysis. Unfortunately, classic theory assumes a single simulation response that is normally and independently distributed with a constant variance; moreover, the regression (meta)model of the simulation model?s I/O behaviour is assumed to have residuals with zero means. This article addresses the following questions: (i) How realistic are these assumptions, in 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 do 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 2007-09.

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Date of creation: 2007
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Handle: RePEc:dgr:kubcen:200709

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Web page: http://center.uvt.nl

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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

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  1. van Beers, Wim C.M. & Kleijnen, Jack P.C., 2008. "Customized sequential designs for random simulation experiments: Kriging metamodeling and bootstrapping," European Journal of Operational Research, Elsevier, vol. 186(3), pages 1099-1113, May. [Downloadable!] (restricted)
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  2. 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!]
  3. 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)
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