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Robust discrimination designs

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  • Douglas P. Wiens

Abstract

We study the construction of experimental designs, the purpose of which is to aid in the discrimination between two possibly non-linear regression models, each of which might be only approximately specified. A rough description of our approach is that we impose neighbourhood structures on each regression response and determine the members of these neighbourhoods which are least favourable in the sense of minimizing the Kullback-Leibler divergence. Designs are obtained which maximize this minimum divergence. Both static and sequential approaches are studied. We then consider sequential designs whose purpose is initially to discriminate, but which move their emphasis towards efficient estimation or prediction as one model becomes favoured over the other. Copyright (c) 2009 Royal Statistical Society.

Suggested Citation

  • Douglas P. Wiens, 2009. "Robust discrimination designs," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(4), pages 805-829.
  • Handle: RePEc:bla:jorssb:v:71:y:2009:i:4:p:805-829
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    File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9868.2009.00711.x
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    References listed on IDEAS

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    1. Holger Dette, 2004. "A comparison of sequential and non-sequential designs for discrimination between nested regression models," Biometrika, Biometrika Trust, vol. 91(1), pages 165-176, March.
    2. Wiens, Douglas P., 1991. "Designs for approximately linear regression: two optimality properties of uniform designs," Statistics & Probability Letters, Elsevier, vol. 12(3), pages 217-221, September.
    3. Biedermann, Stefanie & Dette, Holger, 2001. "Optimal designs for testing the functional form of a regression via nonparametric estimation techniques," Statistics & Probability Letters, Elsevier, vol. 52(2), pages 215-224, April.
    4. Dette, Holger & Titoff, Stefanie, 2008. "Optimal discrimination designs," Technical Reports 2008,06, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
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    Cited by:

    1. Ghosh, Subir & Dutta, Santanu, 2013. "Robustness of designs for model discrimination," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 193-203.
    2. Duarte, Belmiro P.M. & Wong, Weng Kee & Atkinson, Anthony C., 2015. "A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination," Journal of Multivariate Analysis, Elsevier, vol. 135(C), pages 11-24.

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