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Statistical Modelling pp 11–18Cite as

Some Topics in Optimum Experimental Design for Generalized Linear Models

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Part of the book series: Lecture Notes in Statistics ((LNS,volume 104))

Abstract

Optimum experimental designs for generalized linear models are found by applying the methods for normal theory regression models to the information matrix for weighted least squares. The weights are those in the iterative fitting of the model. Examples for logistic regression with two variables illustrate the differences between design for normal theory models and that for other GLMs.

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References

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Authors
  1. Anthony C. Atkinson
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Editor information

Editors and Affiliations

  1. Institut für Statistik, Universität Innsbruck, Christoph-Probst-Platz, Innsbruck, A-6020, Austria

    Gilg U. H. Seeber  & Gabriele Steckel-Berger  & 

  2. Centre for Applied Statistics, Lancaster University Fylde College, Lancaster, LA1 4YF, England

    Brian J. Francis

  3. Institut für Statistik, Wirtschaftsuniversität, Augasse 2-6, Wien, A-1090, Austria

    Reinhold Hatzinger

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© 1995 Springer Science+Business Media New York

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Atkinson, A.C. (1995). Some Topics in Optimum Experimental Design for Generalized Linear Models. In: Seeber, G.U.H., Francis, B.J., Hatzinger, R., Steckel-Berger, G. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 104. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0789-4_2

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  • DOI: https://doi.org/10.1007/978-1-4612-0789-4_2

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94565-1

  • Online ISBN: 978-1-4612-0789-4

  • eBook Packages: Springer Book Archive

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