Augmenting supersaturated designs with Bayesian D-optimality

Alex J. Gutman, Edward D. White, Dennis K.J. Lin, Raymond R. Hill

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A methodology is developed to add runs to existing supersaturated designs. The technique uses information from the analysis of the initial experiment to choose the best possible follow-up runs. After analysis of the initial data, factors are classified into one of three groups: primary, secondary, and potential. Runs are added to maximize a Bayesian D-optimality criterion to increase the information gained about those factors. Simulation results show the method can outperform existing supersaturated design augmentation strategies that add runs without analyzing the initial response variables.

Original languageEnglish (US)
Pages (from-to)1147-1158
Number of pages12
JournalComputational Statistics and Data Analysis
Volume71
DOIs
StatePublished - Mar 2014

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Augmenting supersaturated designs with Bayesian D-optimality'. Together they form a unique fingerprint.

Cite this