Geometric Data Analysis

Henry Rouanet

ISBN 10: 9048166195 / ISBN 13: 9789048166190

Edité par Springer Netherlands, Springer Netherlands Jan 2011, 2011

A propos de cet article

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 488 pp. Englisch.

N° de réf. du vendeur 9789048166190

Détails bibliographiques

• Titre: Geometric Data Analysis
• Éditeur: Springer Netherlands, Springer Netherlands Jan 2011
• Date de parution: 2011
• Langue: anglais
• ISBN à 10 chiffres: 9048166195
• ISBN à 13 chiffres: 9789048166190
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

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.