Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis
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.
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Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis
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.
109.99 In Stock
Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis

Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis

Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis

Geometric Data Analysis: From Correspondence Analysis to Structured Data Analysis

Paperback(Softcover reprint of hardcover 1st ed. 2004)

$109.99 
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Overview

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.

Product Details

ISBN-13: 9789048166190
Publisher: Springer Netherlands
Publication date: 11/19/2010
Edition description: Softcover reprint of hardcover 1st ed. 2004
Pages: 475
Product dimensions: 6.30(w) x 9.45(h) x 0.04(d)

Table of Contents

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.
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