Abstract

http://ssrn.com/abstract=919761
 
 

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


Jack P. C. Kleijnen


Tilburg University, CentER

May 2006

CentER Discussion Paper No. 2006-50

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?

Number of Pages in PDF File: 20

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

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

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Date posted: July 26, 2006  

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: http://ssrn.com/abstract=919761 or http://dx.doi.org/10.2139/ssrn.919761

Contact Information

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)
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