Geometric Data Analysis [electronic resource] : From Correspondence Analysis to Structured Data Analysis /
Le Roux, Brigitte.author.
Rouanet, Henry.author.
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.
Description based upon print version of record.
Includes bibliographical references (p. [451]-463) and indexes.
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.
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.
Probabilities.
Economics.
Management science.
Statistics .
Economic theory.
Computational linguistics.
Probability Theory and Stochastic Processes.
Economics, general.
Statistics, general.
Economic Theory/Quantitative Economics/Mathematical Methods.
Statistics for Life Sciences, Medicine, Health Sciences.
Computational Linguistics.