Geometric Data Analysis (eBook)

From Correspondence Analysis to Structured Data Analysis
eBook Download: PDF
2006 | 2004
XI, 475 Seiten
Springer Netherland (Verlag)
978-1-4020-2236-4 (ISBN)

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Geometric Data Analysis - Brigitte Le Roux, Henry Rouanet
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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.
Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzecri 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.

- Foreword; Patrick Suppes. Preface. - 1: Overview of Geometric Data Analysis. 1.1. CA of a Historical Data Set. 1.2. The Three Key Ideas of GDA. 1.3. Three Paradigms of GDA. 1.4. Historical Sketch. 1.5. Methodological Strong Points. 1.6. From Descriptive to Inductive Analysis. 1.7. Organization of the Book. - 2: Correspondence Analysis (CA). 2.1. Measure vs. Variable Duality. 2.2. Measure over a Cartesian Product. 2.3. Correspondence Analysis. 2.4. Extensions and Concluding Comments. Exercises. - 3: Euclidean Cloud. 3.1. Basic Statistics. 3.2. Projected Clouds. 3.3. Principle Directions. 3.4. Principle Hyperellipsoids. 3.5. Between and within Clouds. 3.6. Euclidean Classification. 3.7. Matrix Formulas. - 4: Principal Component Analysis (PCA). 4.1. Biweighted PCA. 4.2. Simple PCA. 4.3. Standard PCA. 4.4. General PCA. 4.5. PCA of a Table of Measures. 4.6. Methodology of PCA. - 5: Multiple Correspondence Analysis (MCA). 5.1. Standard MCA. 5.2. Specific MCA. 5.3. Methodology of MCA. 5.4. The Culture Example. Exercises. - 6: Structured Data Analysis. 6.1. Structuring Factors. 6.2. Analysis of Comparisons. 6.3. Additive and Interation Clouds. 6.4. Related Topics. - 7: Stability of a Euclidean Cloud. 7.1. Stability and Grouping. 7.2. Influence of aGroup of Points. 7.3. Change of Metric. 7.4. Influence of a Variable. 7.5. Basic Theorems. - 8: Inductive Data Analysis. 8.1. Influence in Multivariate Statistics. 8.2. Univariate Effects. 8.3. Combinatorial Inference. 8.4. Bayesian Data Analysis. 8.5. Inductive GDA. 8.6. Guidelines for Inductive Analysis. - 9: Research Case Studies. 9.1. Parkinson Study. 9.2. French Political Space. 9.3. EPGY Study. 9.4. About Software. - 10: Mathematical Bases. 10.1. Matrix Operations. 10.2. Finite-dimensional Vector Space. 10.3. Euclidean Vector Space. 10.4. Multidimensional Geometry. 10.5. Spectral Theorem. - Bibliography. - Index. Name Index. Symbol Index. Subject Index.

Erscheint lt. Verlag 16.1.2006
Zusatzinfo XI, 475 p.
Verlagsort Dordrecht
Sprache englisch
Themenwelt Mathematik / Informatik Mathematik Statistik
Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
Medizin / Pharmazie Allgemeines / Lexika
Technik
Wirtschaft Volkswirtschaftslehre
Schlagworte Analysis • Analysis of Variance • classification • Data Analysis • linear algebra • measure • Multivariate Statistics • Principal Component Analysis
ISBN-10 1-4020-2236-0 / 1402022360
ISBN-13 978-1-4020-2236-4 / 9781402022364
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