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B. Le Roux and H. Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis, Dordrecht, Kluwer, 2004, pp. xi + 475.

B. Le Roux and H. Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured... Journal of Classification 25:137-141 (2008) DOI: 10.1007/s00357-008-9007-7 BOOK REVIEW B. Le Roux and H. Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis, Dordrecht, Kluwer, 2004, pp. xi + 475. The term “Geometric Data Analysis” is due to Patrick Suppes (Stan- ford) who wrote a Foreword for this encyclopedic view of Correspondence Analysis. The uniqueness of this work lies in the detailed conceptual frame- work, and in showing how, where and why statistical inference methods come into play. As a data analysis methodology, Correspondence Analysis is formal and geometric. The former follows from the fact that mathematical “struc- tures govern procedures”, the mathematics carry the burden of demonstra- tion, and ultimately “this means a fantastic saving in intellectual invest- ment”. The latter is due to the focus on clouds of points in geometric spaces. Unlike the sampling-oriented approach of a good deal of statisti- cal data analysis, including multivariate data analysis, for Correspondence Analysis, “description comes first” since statistics is not reducible to proba- bility. The word “model” is used, in general, in lots and lots of senses (statistical, mathematical, physical models; mixture model; linear model; noise model; neural network model; sparse decomposition model; even data model). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Classification Springer Journals

B. Le Roux and H. Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis, Dordrecht, Kluwer, 2004, pp. xi + 475.

Murtagh, Fionn
Journal of Classification , Volume 25 (1) – Jun 26, 2008
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References (11)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer Science+Business Media, LLC
Subject
Statistics; Marketing ; Psychometrics; Signal, Image and Speech Processing; Bioinformatics; Pattern Recognition; Statistical Theory and Methods
ISSN
0176-4268
eISSN
1432-1343
DOI
10.1007/s00357-008-9007-7
Publisher site
See Article on Publisher Site

Abstract

Journal of Classification 25:137-141 (2008) DOI: 10.1007/s00357-008-9007-7 BOOK REVIEW B. Le Roux and H. Rouanet, Geometric Data Analysis, From Correspondence Analysis to Structured Data Analysis, Dordrecht, Kluwer, 2004, pp. xi + 475. The term “Geometric Data Analysis” is due to Patrick Suppes (Stan- ford) who wrote a Foreword for this encyclopedic view of Correspondence Analysis. The uniqueness of this work lies in the detailed conceptual frame- work, and in showing how, where and why statistical inference methods come into play. As a data analysis methodology, Correspondence Analysis is formal and geometric. The former follows from the fact that mathematical “struc- tures govern procedures”, the mathematics carry the burden of demonstra- tion, and ultimately “this means a fantastic saving in intellectual invest- ment”. The latter is due to the focus on clouds of points in geometric spaces. Unlike the sampling-oriented approach of a good deal of statisti- cal data analysis, including multivariate data analysis, for Correspondence Analysis, “description comes first” since statistics is not reducible to proba- bility. The word “model” is used, in general, in lots and lots of senses (statistical, mathematical, physical models; mixture model; linear model; noise model; neural network model; sparse decomposition model; even data model).

Journal

Journal of Classification – Springer Journals

Published: Jun 26, 2008

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