A two-stage Bayesian model selection strategy for supersaturated designs

Scott D. Beattie, Duncan K.H. Fong, Dennis K.J. Lin

Research output: Contribution to specialist publicationArticle

69 Scopus citations

Abstract

In early stages of experimentation, one often has many candidate factors of which only few have significant influence on the response. Supersaturated designs can offer important advantages. However, standard regression techniques of fitting a prediction line using all candidate variables fail to analyze data from such designs. Stepwise regression may be used but has drawbacks as reported in the literature. A two-stage Bayesian model selection strategy, able to keep all possible models under consideration while providing a level of robustness akin to Bayesian analyses incorporating noninformative priors, is proposed. The strategy is demonstrated on a well-known dataset and compared to competing methods via simulation.

Original languageEnglish (US)
Pages55-63
Number of pages9
Volume44
No1
Specialist publicationTechnometrics
DOIs
StatePublished - Feb 2002

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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