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Invariance and Equivariance in Experimental Design for Nonlinear Models

  • Martin RadloffEmail author
  • Rainer Schwabe
Conference paper
Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

In this note we exhibit the usefulness of invariance considerations in experimental design in the context of nonlinear models. Therefor we examine the equivariance of locally optimal designs and their criteria functions and establish the optimality of invariant designs with respect to robust criteria like weighted or maximin optimality, which avoid parameter dependence.

Keywords

Optimal Design Prior Distribution Information Matrix Linear Predictor Design Region 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute for Mathematical StochasticsOtto-von-Guericke-UniversityMagdeburgGermany

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