White Noise Assumptions Revisited: Regression Models and Statistical Designs for Simulation Practice
Jack P. C. Kleijnen
Tilburg University, CentER
CentER Discussion Paper No. 2006-50
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, C44working papers series
Date posted: July 26, 2006
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