Designing experiments for nonlinear models - An introduction

Rachel T. Johnson, Douglas Montgomery

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


We illustrate the construction of Bayesian D-optimal designs for nonlinear models and compare the relative efficiency of standard designs with these designs for several models and prior distributions on the parameters. Through a relative efficiency analysis, we show that standard designs can perform well in situations where the nonlinear model is intrinsically linear. However, if the model is nonlinear and its expectation function cannot be linearized by simple transformations, the nonlinear optimal design is considerably more efficient than the standard design.

Original languageEnglish (US)
Pages (from-to)431-441
Number of pages11
JournalQuality and Reliability Engineering International
Issue number5
StatePublished - Jul 2010


  • Bayesian D-optimal
  • Factorial design
  • Optimal design

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research


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