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Optimal discrimination designs

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  • Dette, Holger
  • Titoff, Stefanie

Abstract

We consider the problem of constructing optimal designs for model discrimination between competing regression models. Various new properties of optimal designs with respect to the popular T-optimality criterion are derived, which in many circumstances allow an explicit determination of T-optimal designs. It is also demonstrated, that in nested linear models the number of support points of T-optimal designs is usually too small to estimate all parameters in the extended model. In many cases T-optimal designs are usually not unique, and we give a characterization of all T-optimal designs. Finally, T-optimal designs are compared with optimal discriminating designs with respect to alternative criteria by means of a small simulation study.

Suggested Citation

  • Dette, Holger & Titoff, Stefanie, 2008. "Optimal discrimination designs," Technical Reports 2008,06, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    • Handle: RePEc:zbw:sfb475:200806
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    References listed on IDEAS

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    1. J. López‐Fidalgo & C. Tommasi & P. C. Trandafir, 2007. "An optimal experimental design criterion for discriminating between non‐normal models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 231-242, April.
    2. Holger Dette & Viatcheslav Melas & Andrey Pepelyshev, 2006. "Local c- and E-optimal Designs for Exponential Regression Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(2), pages 407-426, June.
    3. Dariusz Uciński & Barbara Bogacka, 2005. "T‐optimum designs for discrimination between two multiresponse dynamic models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 3-18, February.
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    Cited by:

    1. Douglas P. Wiens, 2009. "Robust discrimination designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 805-829, September.

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