Model robust response surface designs: Scaling two-level factorials

Research output: Contribution to journalArticlepeer-review

Abstract

Response surface methods use simple graduating functions to study the relationship between an experimental response variable and a set of continuous explanatory variables. In designing a response surface study, an experimenter must decide how far apart to set the levels of each factor, i.e. how to scale the design. A good choice should be sensitive to the fact that the graduating function is only an approximation to the true response function. A Bayesian model is proposed that makes explicit assumptions about inadequacy of an assumed model and a design criterion based on the model leads to reasonable choices of scale for two-level factorial designs. The choice of scale is found to insensitive to the prior distributions in the model.

Original languageEnglish
Pages (from-to)513-526
Number of pages14
JournalBiometrika
Volume72
Issue number3
DOIs
StatePublished - Dec 1985

Keywords

  • Bayesian model
  • Design scale
  • Factorial experiment
  • Model robust design
  • Response surface design

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