Follow-up designs to resolve confounding in multifactor experiments

R. Daniel Daniel, David M. Steinberg, George Box

Research output: Contribution to journalArticlepeer-review

Abstract

Fractional factorial, Plackett-Burman, and other multifactor designs are often effective in practice due to factor sparsity. That is, just a few of the many factors studied will have major effects. In those active factors, these designs can have high resolution. We have previously developed a Bayesian method based on the idea of model discrimination that uncovers the active factors. Sometimes, the results of a fractional experiment are ambiguous due to confounding among the possible effects, and more than one model may be consistent with the data. Within the Bayesian construct, we have developed a method for designing a follow-up experiment to resolve this ambiguity. The idea is to choose runs that allow maximum discrimination among the plausible models. This method is more general than methods that algebraically decouple aliased interactions and more appropriate than optimal design methods that require specification of a single model. The method is illustrated through examples of fractional experiments.

Original languageEnglish
Pages (from-to)303-313
Number of pages11
JournalTechnometrics
Volume38
Issue number4
DOIs
StatePublished - Nov 1996

Keywords

  • Bayesian models
  • Design augmentation
  • Factor sparsity
  • Model discrimination
  • Screening designs

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