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Efficient discriminating design for a class of nested polynomial regression models

  • Min-Hsiao Tsai

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    This paper studies efficient designs for simultaneous model discrimination among polynomial regression models up to degree k. Based on the $${\Phi_{\boldsymbol{\beta}}}$$ -optimality criterion proposed by Dette (Ann Stat 22:890–903, 1994 ), a maximin $${\Phi_{\boldsymbol{\beta}^{*}}}$$ -optimal discriminating design is derived in terms of canonical moments for $${k\in\mathbb{N}}$$ . Theoretical and numerical results show that the proposed design performs well for model discrimination in most of the considered models. Copyright Springer-Verlag 2012

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    File URL: http://hdl.handle.net/10.1007/s00184-011-0353-9
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    Article provided by Springer in its journal Metrika.

    Volume (Year): 75 (2012)
    Issue (Month): 6 (August)
    Pages: 809-817

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    Handle: RePEc:spr:metrik:v:75:y:2012:i:6:p:809-817
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    1. Dette, Holger & Franke, Tobias, 2000. "Constrained D- and D1-optimal designs for polynomial regression," Technical Reports 2000,37, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Stefanie Biedermann & Holger Dette & Philipp Hoffmann, 2009. "Constrained optimal discrimination designs for Fourier regression models," Annals of the Institute of Statistical Mathematics, Springer, vol. 61(1), pages 143-157, March.
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