Bayesian D-optimal supersaturated designs

Bradley Jones, Dennis K.J. Lin, Christopher J. Nachtsheim

Research output: Contribution to journalArticlepeer-review

49 Scopus citations


We introduce a new class of supersaturated designs using Bayesian D-optimality. The designs generated using this approach can have arbitrary sample sizes, can have any number of blocks of any size, and can incorporate categorical factors with more than two levels. In side by side diagnostic comparisons based on the E (s2) criterion for two-level experiments having even sample size, our designs either match or out-perform the best designs published to date. The generality of the method is illustrated with quality improvement experiment with 15 runs and 20 factors in 3 blocks.

Original languageEnglish (US)
Pages (from-to)86-92
Number of pages7
JournalJournal of Statistical Planning and Inference
Issue number1
StatePublished - Jan 1 2008

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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