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Analysis of symmetric cross-classifications

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Statistical Modelling

Part of the book series: Lecture Notes in Statistics ((LNS,volume 57))

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Abstract

Analysis of data involving symmetrically classified responses involves log-linear models with nonstandard parmetrization. These can be specified in an extended version of symbolic notation that is available in GLIM, so that suitable definition of qualitative and quantitative terms and the recognition of nesting relations among the terms lead to a unified treatment of a large number of previously proposed methods. This approach may be extended to (i) comparison of symmetric tables in different populations and (ii) modelling the effects of symmetrically cross-classified factors on response with a distribution from exponential family. Illustrative applications involve comparison of eye vision among men and women and dependence of fertility on father’s and respondent’s social class.

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Author information

Authors and Affiliations

  1. Institute of Statistics, University of Copenhagen, Studiestraede 6, DK-1455, Copenhagen K, Denmark

    A. Jan Kutylowski (Nordic Research Fellow)

Authors
  1. A. Jan Kutylowski
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Editor information

Editors and Affiliations

  1. Istituto di Biometria e Statistica Medica, Via Venezian, 1, 20133, Milano, Italy

    Adriano Decarli

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

    Brian J. Francis

  3. Polytechnic of North London, Holloway Road, N7 8DB, London, UK

    Robert Gilchrist

  4. Insitut für Statistik, Universität Innsbruck, Innrain 52, 6020, Innsbruck, Austria

    Gilg U. H. Seeber

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© 1989 Springer-Verlag Berlin Heidelberg

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Kutylowski, A.J. (1989). Analysis of symmetric cross-classifications. In: Decarli, A., Francis, B.J., Gilchrist, R., Seeber, G.U.H. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 57. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-3680-1_22

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  • DOI: https://doi.org/10.1007/978-1-4612-3680-1_22

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-97097-4

  • Online ISBN: 978-1-4612-3680-1

  • eBook Packages: Springer Book Archive

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