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

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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?
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages21
Volume2006-50
Publication statusPublished - 2006

Publication series

NameCentER Discussion Paper
Volume2006-50

Keywords

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

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