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

CentER Discussion Paper No. 2006-50

20 Pages Posted: 26 Jul 2006  

Jack P. C. Kleijnen

Tilburg University, CentER

Date Written: May 2006

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?

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

JEL Classification: C0, C1, C9, C15, C44

Suggested Citation

Kleijnen, Jack P. C., White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice (May 2006). CentER Discussion Paper No. 2006-50. Available at SSRN: https://ssrn.com/abstract=919761 or http://dx.doi.org/10.2139/ssrn.919761

Jack P.C. Kleijnen (Contact Author)

Tilburg University, CentER ( email )

P.O. Box 90153
Tilburg, 5000 LE
Netherlands
+31 13 4662029 (Phone)
+31 13 4663377 (Fax)

HOME PAGE: http://https://sites.google.com/site/kleijnenjackpc/

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