Large supersaturated designs

K. M. Eskridge, S. G. Gilmour, R. Mead, N. A. Butler, D. A. Travnicek

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

A supersaturated design (SSD) is an experimental plan, useful for evaluating the main effects of m factors with n experimental units when m > n - 1, each factor has two levels and when the first-order effects of only a few factors are expected to have dominant effects on the response. Use of these plans can be extremely cost-effective when it is necessary to screen hundreds or thousands of factors with a limited amount of resources. In this article we describe how to use cyclic balanced incomplete block designs and regular graph designs to construct E(s 2) optimal and near optimal SSDs when m is a multiple of n - 1. We also provide a table that can be used to construct these designs for screening thousands of factors. We also explain how to obtain SSDs when m is not a multiple of n - 1. Using the table and the approaches given in this paper, SSDs can be developed for designs with up to 24 runs and up to 12,190 factors.

Original languageEnglish (US)
Pages (from-to)525-542
Number of pages18
JournalJournal of Statistical Computation and Simulation
Volume74
Issue number7
DOIs
StatePublished - Jul 2004

Keywords

  • Computer-aided design
  • Design of experiments
  • Incomplete block design
  • Regular graph design
  • Screening design

ASJC Scopus subject areas

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

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