Experimental designs for model discrimination

Friedrich Pukelsheim, James L. Rosenberger

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

We present designs that perform well for several objectives simultaneously. Three different approaches are discussed: to augment a given design in an optimal way, to evaluate a mixture of the various criteria, and to optimize one objective subject to achieving a prescribed efficiency level for the others. Our sample designs are for the situation of discriminating between a second- and third-degree polynomial fit, under the D-criterion and geometric mixtures of D-criteria.

Original languageEnglish (US)
Pages (from-to)642-649
Number of pages8
JournalJournal of the American Statistical Association
Volume88
Issue number422
DOIs
StatePublished - Jun 1993

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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