Identifying locally optimal designs for nonlinear models: A simple extension with profound consequences

Min Yang, John Stufken

Research output: Contribution to journalArticle

23 Scopus citations

Abstract

We extend the approach in [Ann. Statist. 38 (2010) 2499-2524] for identifying locally optimal designs for nonlinear models. Conceptually the extension is relatively simple, but the consequences in terms of applications are profound. As we will demonstrate, we can obtain results for locally optimal designs under many optimality criteria and for a larger class of models than has been done hitherto. In many cases the results lead to optimal designs with the minimal number of support points.

Original languageEnglish (US)
Pages (from-to)1665-1681
Number of pages17
JournalAnnals of Statistics
Volume40
Issue number3
DOIs
StatePublished - Jun 1 2012

Keywords

  • Locally optimal
  • Loewner ordering
  • Principal submatrix
  • Support points

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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