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Optimal experimental designs for inverse quadratic regression models

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  • Dette, Holger
  • Kiss, Christine

Abstract

In this paper optimal experimental designs for inverse quadratic regression models are determined. We consider two dfferent parameterizations of the model and investigate local optimal designs with respect to the c-, D-and E-criteria, which reflect various aspects of the precision of the maximum likelihood estimator for the parameters in inverse quadratic regression models. In particular it is demonstrated that for a sufficiently large design space geometric allocation rules are optimal with respect to many optimality criteria. Moreover, in numerous cases the designs with respect to the different criteria are supported at the same points. Finally, the effiiencies of different optimal designs with respect to various optimality criteria are studied, and the effiiency of some commonly used designs are investigated.

Suggested Citation

  • Dette, Holger & Kiss, Christine, 2007. "Optimal experimental designs for inverse quadratic regression models," Technical Reports 2007,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200737
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    References listed on IDEAS

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    1. Dette, Holger & Biedermann, Stefanie, 2003. "Robust and Efficient Designs for the Michaelis-Menten Model," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 679-686, January.
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