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Symbolic Regression Analysis

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Classification, Clustering, and Data Analysis

Abstract

Billard and Diday (2000) developed procedures for fitting a regression equation to symbolic interval-valued data. The present paper compares that approach with several possible alternative models using classical techniques; the symbolic regression approach is preferred. Thence, a regression approach is provided for symbolic histogram-valued data. The results are illustrated with a medical data set.

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References

  • BERTRAND, P. and GOUPIL, F. (2000): Descriptive Statisitcs for Symbolic Data. In: Analysis of Symbolic Data Sets (eds. H.-H. Bock and E. Diday ), Springer, 103–124.

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  • BILLARD, L. and DIDAY, E. (2000): Regression Analysis for Interval-Valued Data. In: Data Analysis, Classification, and Related Methods (eds. H. A. L. Kiers, J.-P. Rasson, P. J. F. Groenen and M. Schader ), Springer, 369–374.

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  • BILLARD, L. and DIDAY, E. (2001): From the Statistics of Data to the Statistics of Knowledge: Symbolic Data Analysis, submitted.

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  • RODRIGUEZ, O. (2001): Classification et Modéles Linéaires en Analyse des Données Symboliques. Doctoral Thesis, University of Paris, Dauphine.

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Author information

Authors and Affiliations

  1. Department of Statistics, University of Georgia, Athens, GA, 30602, USA

    Lynne Billard

  2. CEREMADE, Universite de Paris 9 Dauphine, 75775, Paris Cedex 16, France

    Edwin Diday

Authors
  1. Lynne Billard
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  2. Edwin Diday
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Editor information

Editors and Affiliations

  1. Wroclaw University of Economics, ul. Komandorska 118/120, 53-345, Wroclaw, Poland

    Krzysztof Jajuga

  2. Department of Statistics, Cracow University of Economics, ul. Rakowicka 27, 31-510, Cracow, Poland

    Andrzej Sokołowski

  3. Institute of Statistics, Technical University of Aachen, Wuellnerstrasse 3, 52056, Aachen, Germany

    Hans-Hermann Bock

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© 2002 Springer-Verlag Berlin Heidelberg

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Cite this paper

Billard, L., Diday, E. (2002). Symbolic Regression Analysis. In: Jajuga, K., Sokołowski, A., Bock, HH. (eds) Classification, Clustering, and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-56181-8_31

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  • DOI: https://doi.org/10.1007/978-3-642-56181-8_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43691-1

  • Online ISBN: 978-3-642-56181-8

  • eBook Packages: Springer Book Archive

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