Advertisement

Symbolic Regression Analysis

  • Lynne Billard
  • Edwin Diday
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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.

Keywords

Prediction Interval Symbolic Data Symbolic Regression Description Vector Histogram Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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.Google Scholar
  2. 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.CrossRefGoogle Scholar
  3. BILLARD, L. and DIDAY, E. (2001): From the Statistics of Data to the Statistics of Knowledge: Symbolic Data Analysis, submitted.Google Scholar
  4. RODRIGUEZ, O. (2001): Classification et Modéles Linéaires en Analyse des Données Symboliques. Doctoral Thesis, University of Paris, Dauphine.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Lynne Billard
    • 1
  • Edwin Diday
    • 2
  1. 1.Department of StatisticsUniversity of GeorgiaAthensUSA
  2. 2.CEREMADEUniversite de Paris 9 DauphineParis Cedex 16France

Personalised recommendations