Chapter 19. Symbolic Linear Regression Methodology

  1. Edwin Diday3,
  2. Monique Noirhomme-Fraiture4
  1. Filipe Afonso1,
  2. Lynne Billard2,
  3. Edwin Diday3,
  4. Mehdi Limam1

Published Online: 28 JAN 2008

DOI: 10.1002/9780470723562.ch19

Book Title

Symbolic Data Analysis and the SODAS Software

Symbolic Data Analysis and the SODAS Software

Additional Information

How to Cite

Afonso, F., Billard, L., Diday, E. and Limam, M. (2008) Symbolic Linear Regression Methodology, in Symbolic Data Analysis and the SODAS Software (eds E. Diday and M. Noirhomme-Fraiture), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470723562.ch19

Editor Information

  1. 3

    Université Paris IX-Dauphine, LISE-CEREMADE, Place du Marechal de Lattre de Tassigny, Paris Cedex 16, France F-75775

  2. 4

    Facultés Universitaires Notre-Dame de la Paix, Faculté d'Informatique, Rue Grandgagnage, 21, Namur, Belgium, B-5000

Author Information

  1. 1

    Université Paris IX-Dauphine, LISE-CEREMADE, Place du Maréchal de Lattre de Tassigny, Paris Cedex 16, France, F-75775

  2. 2

    University of Georgia, Athens, USA, GA 30602-1952

  3. 3

    Université Paris IX-Dauphine, LISE-CEREMADE, Place du Marechal de Lattre de Tassigny, Paris Cedex 16, France F-75775

Publication History

  1. Published Online: 28 JAN 2008
  2. Published Print: 18 JAN 2007

ISBN Information

Print ISBN: 9780470018835

Online ISBN: 9780470723562

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CHAPTER TOOLS

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Keywords:

  • hierarchical dependent variables;
  • taxonomical explanatory variables;
  • symbolic linear regression;
  • taxonomic variable ‘age’;
  • haematocrit variables;
  • haemogroup variable;
  • taxonomical explanatory variables;
  • histogram-valued symbolic data;
  • race descriptor;
  • work–demographic–medical data

Summary

This chapter contains sections titled:

  • Introduction

  • State of the art

  • Taxonomic and hierarchical dependent variables

  • Linear regression for taxonomic variables

  • Linear regression for hierarchical variables

  • SREG module

  • Applications of symbolic linear regression

  • Conclusions and perspectives

  • References

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