Geometric Data Analysis

From Correspondence Analysis to Structured Data Analysis
Softcover Nachdruck of hardcover 1. Auflage 2004
ISBN: 978-90-481-6619-0
Verlag: Springer Netherlands

Buch, Englisch, 475 Seiten, Previously published in hardcover, Format (B × H): 160 mm x 240 mm, Gewicht: 772 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Rouanet, Henry

Le Roux, Brigitte

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Ökonometrie
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Wirtschaftswissenschaften Volkswirtschaftslehre Volkswirtschaftslehre Allgemein Wirtschaftstheorie, Wirtschaftsphilosophie

Inhaltsverzeichnis

Overview of Geometric Data Analysis (‘Overview’).- Correspondence Analysis.- Euclidean Cloud.- Principal Component Analysis.- Multiple Correspondence Analysis (MCA).- Structured Data Analysis.- Stability of a Euclidean Cloud.- Inductive Data Analysis.- Research Case Studies.- Mathematical Bases.