Uniform designs limit aliasing

Fred J. Hickernell*, Min Qian Liu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

107 Citations (Scopus)
4 Downloads (Pure)


When fitting a linear regression model to data, aliasing can adversely affect the estimates of the model coefficients and the decision of whether or not a term is significant. Optimal experimental designs give efficient estimators assuming that the true form of the model is known, while robust experimental designs guard against inaccurate estimates caused by model misspecification. Although it is rare for a single design to be both maximally efficient and robust, it is shown here that uniform designs limit the effects of aliasing to yield reasonable efficiency and robustness together. Aberration and resolution measure how well fractional factorial designs guard against the effects of aliasing. Here it is shown that the definitions of aberration and resolution may be generalised to other types of design using the discrepancy.

Original languageEnglish
Pages (from-to)893-904
Number of pages12
Issue number4
Publication statusPublished - 1 Dec 2002
Externally publishedYes

Scopus Subject Areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

User-Defined Keywords

  • Discrepancy
  • Effects aliasing
  • Fractional factorial design
  • Minimum aberration
  • Orthogonal array
  • Reproducing kernel Hubert space
  • Resolution


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