Geometric Data Analysis

From Correspondence Analysis to Structured Data Analysis

  • Brigitte Le Roux
  • Henry Rouanet

Table of contents

  1. Front Matter
    Pages i-xi
  2. Pages 75-128
  3. Pages 251-265
  4. Pages 297-332
  5. Pages 333-418
  6. Pages 419-449
  7. Back Matter
    Pages 451-475

About this book


Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzécri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.


Analysis Analysis of variance Measure Multivariate statistics classification data analysis linear algebra principal component analysis

Authors and affiliations

  • Brigitte Le Roux
    • 1
  • Henry Rouanet
    • 2
  1. 1.MAPS 5 (CNRS) Department of Mathematics and Computer ScienceUniversité René DescartesParisFrance
  2. 2.CRIP 5 Department of Mathematics and Computer ScienceUniversité René DescartesParisFrance

Bibliographic information