CentER Discussion Paper No. 2006-50
20 Pages Posted: 26 Jul 2006
Date Written: May 2006
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: 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