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mODa 11 - Advances in Model-Oriented Design and Analysis pp 217–224Cite as

Invariance and Equivariance in Experimental Design for Nonlinear Models

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Part of the book series: Contributions to Statistics ((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.

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Author information

Authors and Affiliations

  1. Institute for Mathematical Stochastics, Otto-von-Guericke-University, PF 4120, 39016, Magdeburg, Germany

    Martin Radloff & Rainer Schwabe

Authors
  1. Martin Radloff
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  2. Rainer Schwabe
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Corresponding author

Correspondence to Martin Radloff .

Editor information

Editors and Affiliations

  1. Department of Statistics, Technische Universität Dortmund, Dortmund, Germany

    Joachim Kunert

  2. Department of Statistics, Technische Universität Dortmund, Dortmund, Germany

    Christine H. Müller

  3. Department of Statistics, London School of Economics, London, United Kingdom

    Anthony C. Atkinson

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© 2016 Springer International Publishing Switzerland

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Radloff, M., Schwabe, R. (2016). Invariance and Equivariance in Experimental Design for Nonlinear Models. In: Kunert, J., Müller, C., Atkinson, A. (eds) mODa 11 - Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-31266-8_25

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