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An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
| An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo |
| Autore | Beh Eric J. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] |
| Descrizione fisica | 1 online resource (243 pages) : illustrations |
| Disciplina | 519.537 |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico | Correspondence analysis (Statistics) |
| ISBN |
1-119-04196-1
1-119-04448-0 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Data Visualisation -- 1.2 Correspondence Analysis in a "Nutshell" -- 1.3 Data Sets -- 1.3.1 Traditional European Food Data -- 1.3.2 Temperature Data -- 1.3.3 Shoplifting Data -- 1.3.4 Alligator Data -- 1.4 Symmetrical Versus Asymmetrical Association -- 1.5 Notation -- 1.5.1 The Two‐way Contingency Table -- 1.5.2 The Three‐way Contingency Table -- 1.6 Formal Test of Symmetrical Association -- 1.6.1 Test of Independence for Two‐way Contingency Tables -- 1.6.2 The Chi‐squared Statistic for a Two‐way Table -- 1.6.3 Analysis of the Traditional European Food Data -- 1.6.4 The Chi‐squared Statistic for a Three‐way Table -- 1.6.5 Analysis of the Alligator Data -- 1.7 Formal Test of Asymmetrical Association -- 1.7.1 Test of Predictability for Two‐way Contingency Tables -- 1.7.2 The Goodman-Kruskal tau Index -- 1.7.3 Analysis of the Traditional European Food Data -- 1.7.4 Test of Predictability for Three‐way Contingency Tables -- 1.7.5 Marcotorchino's Index -- 1.7.6 Analysis of the Alligator Data -- 1.7.7 The Gray-Williams Index and Delta Index -- 1.8 Correspondence Analysis and R -- 1.9 Overview of the Book -- Part I Classical Analysis of Two Categorical Variables -- Chapter 2 Simple Correspondence Analysis -- 2.1 Introduction -- 2.2 Reducing Multi‐dimensional Space -- 2.2.1 Profiles Cloud of Points -- 2.2.2 Profiles for the Traditional European Food Data -- 2.2.3 Weighted Centred Profiles -- 2.3 Measuring Symmetric Association -- 2.3.1 The Pearson Ratio -- 2.3.2 Analysis of the Traditional European Food Data -- 2.4 Decomposing the Pearson Residual for Nominal Variables -- 2.4.1 The Generalised SVD of γij−1 -- 2.4.2 SVD of the Pearson Ratio's -- 2.4.3 GSVD and the Traditional European Food Data -- 2.5 Constructing a Low‐Dimensional Display.
2.5.1 Standard Coordinates -- 2.5.2 Principal Coordinates -- 2.6 Practicalities of the Low‐Dimensional Plot -- 2.6.1 The Two‐Dimensional Correspondence Plot -- 2.6.2 What is NOT Being Shown in a Two‐Dimensional Correspondence Plot? -- 2.6.3 The Three‐Dimensional Correspondence Plot -- 2.7 The Biplot Display -- 2.7.1 Definition -- 2.7.2 Isometric Biplots of the Traditional European Food Data -- 2.7.3 What is NOT Being Shown in a Two‐Dimensional Biplot? -- 2.8 The Case for No Visual Display -- 2.9 Detecting Statistically Significant Points -- 2.9.1 Confidence Circles and Ellipses -- 2.9.2 Confidence Ellipses for the Traditional European Food Data -- 2.10 Approximate p‐values -- 2.10.1 The Hypothesis Test and its p‐value -- 2.10.2 P‐values and the Traditional European Food Data -- 2.11 Final Comments -- Chapter 3 Non‐Symmetrical Correspondence Analysis -- 3.1 Introduction -- 3.2 Quantifying Asymmetric Association -- 3.2.1 The Goodman-Kruskal tau Index -- 3.2.2 The τ Index and the Traditional European Food Data -- 3.2.3 Weighted Centred Column Profile -- 3.2.4 Profiles of the Traditional European Food Data -- 3.3 Decomposing πi|j for Nominal Variables -- 3.3.1 The Generalised SVD of πi|j -- 3.3.2 GSVD and the Traditional Food Data -- 3.4 Constructing a Low‐Dimensional Display -- 3.4.1 Standard Coordinates -- 3.4.2 Principal Coordinates -- 3.5 Practicalities of the Low‐Dimensional Plot -- 3.5.1 The Two‐Dimensional Correspondence Plot -- 3.5.2 The Three‐Dimensional Correspondence Plot -- 3.6 The Biplot Display -- 3.6.1 Definition -- 3.6.2 The Column Isometric Biplot for the Traditional Food Data -- 3.6.3 The Three‐Dimensional Biplot -- 3.7 Detecting Statistically Significant Points -- 3.7.1 Confidence Circles and Ellipses -- 3.7.2 Confidence Ellipses for the Traditional Food Data -- 3.8 Final Comments. Part II Ordinal Analysis of Two Categorical Variables -- Chapter 4 Simple Ordinal Correspondence Analysis -- 4.1 Introduction -- 4.2 A Simple Correspondence Analysis of the Temperature Data -- 4.3 On the Mean and Variation of Profiles with Ordered Categories -- 4.3.1 Profiles of the Temperature Data -- 4.3.2 Defining Scores -- 4.3.3 On the Mean of the Profiles -- 4.3.4 On the Variation of the Profiles -- 4.3.5 Mean and Variation of Profiles for the Temperature Data -- 4.4 Decomposing the Pearson Residual for Ordinal Variables -- 4.4.1 The Bivariate Moment Decomposition of γij−1 -- 4.4.2 BMD and the Temperature Data -- 4.5 Constructing a Low‐Dimensional Display -- 4.5.1 Standard Coordinates -- 4.5.2 Principal Coordinates -- 4.5.3 Practicalities of the Ordered Principal Coordinates -- 4.6 The Biplot Display -- 4.6.1 Definition -- 4.6.2 Ordered Column Isometric Biplot -- 4.6.3 Ordered Row Isometric Biplot -- 4.6.4 Ordered Isometric Biplots for the Temperature Data -- 4.7 Final Comments -- Chapter 5 Ordered Non‐symmetrical Correspondence Analysis -- 5.1 Introduction -- 5.2 The Goodman-Kruskal tau Index Revisited -- 5.3 Decomposing πi|j for Ordinal and Nominal Variables -- 5.3.1 The Hybrid Decomposition of πi|j -- 5.3.2 Hybrid Decomposition and the Shoplifting Data -- 5.4 Constructing a Low‐Dimensional Display -- 5.4.1 Standard Coordinates -- 5.4.2 Principal Coordinates -- 5.5 The Biplot -- 5.5.1 An Overview -- 5.5.2 Column Isometric Biplot -- 5.5.3 Column Isometric Biplot of the Shoplifting Data -- 5.5.4 Row Isometric Biplot -- 5.5.5 Row Isometric Biplot of the Shoplifting Data -- 5.5.6 Distance Measures and the Row Isometric Biplots -- 5.6 Some Final Words -- Part III Analysis of Multiple Categorical Variables -- Chapter 6 Multiple Correspondence Analysis -- 6.1 Introduction -- 6.2 Crisp Coding and the Indicator Matrix -- 6.2.1 Crisp Coding. 6.2.2 The Indicator Matrix -- 6.2.3 Crisp Coding and the Alligator Data -- 6.2.4 Application of Multiple Correspondence Analysis using the Indicator Matrix -- 6.3 The Burt Matrix -- 6.4 Stacking -- 6.4.1 A Definition -- 6.4.2 Stacking and the Alligator Data - Lake(Size)× Food -- 6.4.3 Stacking and the Alligator Data - Food(Size)× Lake -- 6.5 Final Comments -- Chapter 7 Multi‐way Correspondence Analysis -- 7.1 An Introduction -- 7.2 Pearson's Residual γijk−1 and the Partition of X2 -- 7.2.1 The Pearson Residual -- 7.2.2 The Partition of X2 -- 7.2.3 Partition of X2 for the Alligator Data -- 7.3 Symmetric Multi‐way Correspondence Analysis -- 7.3.1 Tucker3 Decomposition of γijk−1 -- 7.3.2 T3D and the Analysis of Two Variables -- 7.3.3 On the Choice of the Number of Components -- 7.3.4 Tucker3 Decomposition of γijk−1 and the Alligator Data -- 7.4 Constructing a Low‐Dimensional Display -- 7.4.1 Principal Coordinates -- 7.4.2 The Interactive Biplot -- 7.4.3 Column‐Tube Interactive Biplot for the Alligator Data -- 7.4.4 Row Interactive Biplot for the Alligator Data -- 7.5 The Marcotorchino Residual πi|j,k and the Partition of τM -- 7.5.1 The Marcotrochino Residual -- 7.5.2 The Partition of τM -- 7.5.3 Partition of τM for the Alligator Data -- 7.6 Non‐symmetrical Multi‐way Correspondence Analysis -- 7.6.1 Tucker3 Decomposition of πi|j,k -- 7.6.2 Tucker3 Decomposition of πi|j,k and the Alligator Data -- 7.7 Constructing a Low‐Dimensional Display -- 7.7.1 On the Choice of Coordinates -- 7.7.2 Column-Tube Interactive Biplot for the Alligator Data -- 7.8 Final Comments -- References -- Author Index -- Subject Index -- EULA. |
| Record Nr. | UNINA-9910676630003321 |
Beh Eric J.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo
| An introduction to correspondence analysis / / Eric J. Beh and Rosaria Lombardo |
| Autore | Beh Eric J. |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] |
| Descrizione fisica | 1 online resource (243 pages) : illustrations |
| Disciplina | 519.537 |
| Collana | Wiley Series in Probability and Statistics |
| Soggetto topico | Correspondence analysis (Statistics) |
| ISBN |
1-119-04196-1
1-119-04448-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- Chapter 1 Introduction -- 1.1 Data Visualisation -- 1.2 Correspondence Analysis in a "Nutshell" -- 1.3 Data Sets -- 1.3.1 Traditional European Food Data -- 1.3.2 Temperature Data -- 1.3.3 Shoplifting Data -- 1.3.4 Alligator Data -- 1.4 Symmetrical Versus Asymmetrical Association -- 1.5 Notation -- 1.5.1 The Two‐way Contingency Table -- 1.5.2 The Three‐way Contingency Table -- 1.6 Formal Test of Symmetrical Association -- 1.6.1 Test of Independence for Two‐way Contingency Tables -- 1.6.2 The Chi‐squared Statistic for a Two‐way Table -- 1.6.3 Analysis of the Traditional European Food Data -- 1.6.4 The Chi‐squared Statistic for a Three‐way Table -- 1.6.5 Analysis of the Alligator Data -- 1.7 Formal Test of Asymmetrical Association -- 1.7.1 Test of Predictability for Two‐way Contingency Tables -- 1.7.2 The Goodman-Kruskal tau Index -- 1.7.3 Analysis of the Traditional European Food Data -- 1.7.4 Test of Predictability for Three‐way Contingency Tables -- 1.7.5 Marcotorchino's Index -- 1.7.6 Analysis of the Alligator Data -- 1.7.7 The Gray-Williams Index and Delta Index -- 1.8 Correspondence Analysis and R -- 1.9 Overview of the Book -- Part I Classical Analysis of Two Categorical Variables -- Chapter 2 Simple Correspondence Analysis -- 2.1 Introduction -- 2.2 Reducing Multi‐dimensional Space -- 2.2.1 Profiles Cloud of Points -- 2.2.2 Profiles for the Traditional European Food Data -- 2.2.3 Weighted Centred Profiles -- 2.3 Measuring Symmetric Association -- 2.3.1 The Pearson Ratio -- 2.3.2 Analysis of the Traditional European Food Data -- 2.4 Decomposing the Pearson Residual for Nominal Variables -- 2.4.1 The Generalised SVD of γij−1 -- 2.4.2 SVD of the Pearson Ratio's -- 2.4.3 GSVD and the Traditional European Food Data -- 2.5 Constructing a Low‐Dimensional Display.
2.5.1 Standard Coordinates -- 2.5.2 Principal Coordinates -- 2.6 Practicalities of the Low‐Dimensional Plot -- 2.6.1 The Two‐Dimensional Correspondence Plot -- 2.6.2 What is NOT Being Shown in a Two‐Dimensional Correspondence Plot? -- 2.6.3 The Three‐Dimensional Correspondence Plot -- 2.7 The Biplot Display -- 2.7.1 Definition -- 2.7.2 Isometric Biplots of the Traditional European Food Data -- 2.7.3 What is NOT Being Shown in a Two‐Dimensional Biplot? -- 2.8 The Case for No Visual Display -- 2.9 Detecting Statistically Significant Points -- 2.9.1 Confidence Circles and Ellipses -- 2.9.2 Confidence Ellipses for the Traditional European Food Data -- 2.10 Approximate p‐values -- 2.10.1 The Hypothesis Test and its p‐value -- 2.10.2 P‐values and the Traditional European Food Data -- 2.11 Final Comments -- Chapter 3 Non‐Symmetrical Correspondence Analysis -- 3.1 Introduction -- 3.2 Quantifying Asymmetric Association -- 3.2.1 The Goodman-Kruskal tau Index -- 3.2.2 The τ Index and the Traditional European Food Data -- 3.2.3 Weighted Centred Column Profile -- 3.2.4 Profiles of the Traditional European Food Data -- 3.3 Decomposing πi|j for Nominal Variables -- 3.3.1 The Generalised SVD of πi|j -- 3.3.2 GSVD and the Traditional Food Data -- 3.4 Constructing a Low‐Dimensional Display -- 3.4.1 Standard Coordinates -- 3.4.2 Principal Coordinates -- 3.5 Practicalities of the Low‐Dimensional Plot -- 3.5.1 The Two‐Dimensional Correspondence Plot -- 3.5.2 The Three‐Dimensional Correspondence Plot -- 3.6 The Biplot Display -- 3.6.1 Definition -- 3.6.2 The Column Isometric Biplot for the Traditional Food Data -- 3.6.3 The Three‐Dimensional Biplot -- 3.7 Detecting Statistically Significant Points -- 3.7.1 Confidence Circles and Ellipses -- 3.7.2 Confidence Ellipses for the Traditional Food Data -- 3.8 Final Comments. Part II Ordinal Analysis of Two Categorical Variables -- Chapter 4 Simple Ordinal Correspondence Analysis -- 4.1 Introduction -- 4.2 A Simple Correspondence Analysis of the Temperature Data -- 4.3 On the Mean and Variation of Profiles with Ordered Categories -- 4.3.1 Profiles of the Temperature Data -- 4.3.2 Defining Scores -- 4.3.3 On the Mean of the Profiles -- 4.3.4 On the Variation of the Profiles -- 4.3.5 Mean and Variation of Profiles for the Temperature Data -- 4.4 Decomposing the Pearson Residual for Ordinal Variables -- 4.4.1 The Bivariate Moment Decomposition of γij−1 -- 4.4.2 BMD and the Temperature Data -- 4.5 Constructing a Low‐Dimensional Display -- 4.5.1 Standard Coordinates -- 4.5.2 Principal Coordinates -- 4.5.3 Practicalities of the Ordered Principal Coordinates -- 4.6 The Biplot Display -- 4.6.1 Definition -- 4.6.2 Ordered Column Isometric Biplot -- 4.6.3 Ordered Row Isometric Biplot -- 4.6.4 Ordered Isometric Biplots for the Temperature Data -- 4.7 Final Comments -- Chapter 5 Ordered Non‐symmetrical Correspondence Analysis -- 5.1 Introduction -- 5.2 The Goodman-Kruskal tau Index Revisited -- 5.3 Decomposing πi|j for Ordinal and Nominal Variables -- 5.3.1 The Hybrid Decomposition of πi|j -- 5.3.2 Hybrid Decomposition and the Shoplifting Data -- 5.4 Constructing a Low‐Dimensional Display -- 5.4.1 Standard Coordinates -- 5.4.2 Principal Coordinates -- 5.5 The Biplot -- 5.5.1 An Overview -- 5.5.2 Column Isometric Biplot -- 5.5.3 Column Isometric Biplot of the Shoplifting Data -- 5.5.4 Row Isometric Biplot -- 5.5.5 Row Isometric Biplot of the Shoplifting Data -- 5.5.6 Distance Measures and the Row Isometric Biplots -- 5.6 Some Final Words -- Part III Analysis of Multiple Categorical Variables -- Chapter 6 Multiple Correspondence Analysis -- 6.1 Introduction -- 6.2 Crisp Coding and the Indicator Matrix -- 6.2.1 Crisp Coding. 6.2.2 The Indicator Matrix -- 6.2.3 Crisp Coding and the Alligator Data -- 6.2.4 Application of Multiple Correspondence Analysis using the Indicator Matrix -- 6.3 The Burt Matrix -- 6.4 Stacking -- 6.4.1 A Definition -- 6.4.2 Stacking and the Alligator Data - Lake(Size)× Food -- 6.4.3 Stacking and the Alligator Data - Food(Size)× Lake -- 6.5 Final Comments -- Chapter 7 Multi‐way Correspondence Analysis -- 7.1 An Introduction -- 7.2 Pearson's Residual γijk−1 and the Partition of X2 -- 7.2.1 The Pearson Residual -- 7.2.2 The Partition of X2 -- 7.2.3 Partition of X2 for the Alligator Data -- 7.3 Symmetric Multi‐way Correspondence Analysis -- 7.3.1 Tucker3 Decomposition of γijk−1 -- 7.3.2 T3D and the Analysis of Two Variables -- 7.3.3 On the Choice of the Number of Components -- 7.3.4 Tucker3 Decomposition of γijk−1 and the Alligator Data -- 7.4 Constructing a Low‐Dimensional Display -- 7.4.1 Principal Coordinates -- 7.4.2 The Interactive Biplot -- 7.4.3 Column‐Tube Interactive Biplot for the Alligator Data -- 7.4.4 Row Interactive Biplot for the Alligator Data -- 7.5 The Marcotorchino Residual πi|j,k and the Partition of τM -- 7.5.1 The Marcotrochino Residual -- 7.5.2 The Partition of τM -- 7.5.3 Partition of τM for the Alligator Data -- 7.6 Non‐symmetrical Multi‐way Correspondence Analysis -- 7.6.1 Tucker3 Decomposition of πi|j,k -- 7.6.2 Tucker3 Decomposition of πi|j,k and the Alligator Data -- 7.7 Constructing a Low‐Dimensional Display -- 7.7.1 On the Choice of Coordinates -- 7.7.2 Column-Tube Interactive Biplot for the Alligator Data -- 7.8 Final Comments -- References -- Author Index -- Subject Index -- EULA. |
| Record Nr. | UNINA-9910807726803321 |
|
Beh Eric J.
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Incorporated, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Visualization and verbalization of data / / edited by Jorg Blasius, University of Bonn, Germany, Michael Greenacre, Universitat Pompeu Fabra, Barcelona, Spain
| Visualization and verbalization of data / / edited by Jorg Blasius, University of Bonn, Germany, Michael Greenacre, Universitat Pompeu Fabra, Barcelona, Spain |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , [2014] |
| Descrizione fisica | 1 online resource (382 p.) |
| Disciplina | 001.4/226 |
| Collana | Chapman and Hall/CRC Computer Science and Data Analysis |
| Soggetto topico |
Information visualization
Correspondence analysis (Statistics) Multiple comparisons (Statistics) |
| ISBN |
0-429-16798-9
1-4665-8981-7 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Foreword; Preface; Editors; Contributors; Prologue: Let the Data Speak!; Chapter 1: Some Prehistory of CARME: Visual Language and Visual Thinking; Chapter 2: Some History of Algebraic Canonical Forms and Data Analysis; Chapter 3: Historical Elements of Correspondence Analysis and Multiple Correspondence Analysis; Chapter 4: History of Nonlinear Principal Component Analysis; Chapter 5: History of Canonical Correspondence Analysis; Chapter 6: History of Multiway Component Analysis and Three-Way Correspondence Analysis
Chapter 7: Past, Present, and Future of Multidimensional ScalingChapter 8: History of Cluster Analysis; Chapter 9: Simple Correspondence Analysis; Chapter 10: Distributional Equivalence and Linguistics; Chapter 11: Multiple Correspondence Analysis; Chapter 12: Structured Data Analysis; Chapter 13: Empirical Construction of Bourdieu's Social Space; Chapter 14: Multiple Factor Analysis:General Presentation and Comparison with STATIS; Chapter 15: Data Doubling and Fuzzy Coding; Chapter 16: Symbolic Data Analysis: A Factorial Approach Based on Fuzzy Coded Data Chapter 17: Group Average Linkage Compared to Ward's Method in Hierarchical ClusteringChapter 18: Analysing a Pair of Tables: Coinertia Analysis and Duality Diagrams; References; Back Cover |
| Record Nr. | UNINA-9910790955503321 |
| Boca Raton : , : CRC Press, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Visualization and verbalization of data / / edited by Jorg Blasius, University of Bonn, Germany, Michael Greenacre, Universitat Pompeu Fabra, Barcelona, Spain
| Visualization and verbalization of data / / edited by Jorg Blasius, University of Bonn, Germany, Michael Greenacre, Universitat Pompeu Fabra, Barcelona, Spain |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , [2014] |
| Descrizione fisica | 1 online resource (382 p.) |
| Disciplina | 001.4/226 |
| Collana | Chapman and Hall/CRC Computer Science and Data Analysis |
| Soggetto topico |
Information visualization
Correspondence analysis (Statistics) Multiple comparisons (Statistics) |
| ISBN |
0-429-16798-9
1-4665-8981-7 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Foreword; Preface; Editors; Contributors; Prologue: Let the Data Speak!; Chapter 1: Some Prehistory of CARME: Visual Language and Visual Thinking; Chapter 2: Some History of Algebraic Canonical Forms and Data Analysis; Chapter 3: Historical Elements of Correspondence Analysis and Multiple Correspondence Analysis; Chapter 4: History of Nonlinear Principal Component Analysis; Chapter 5: History of Canonical Correspondence Analysis; Chapter 6: History of Multiway Component Analysis and Three-Way Correspondence Analysis
Chapter 7: Past, Present, and Future of Multidimensional ScalingChapter 8: History of Cluster Analysis; Chapter 9: Simple Correspondence Analysis; Chapter 10: Distributional Equivalence and Linguistics; Chapter 11: Multiple Correspondence Analysis; Chapter 12: Structured Data Analysis; Chapter 13: Empirical Construction of Bourdieu's Social Space; Chapter 14: Multiple Factor Analysis:General Presentation and Comparison with STATIS; Chapter 15: Data Doubling and Fuzzy Coding; Chapter 16: Symbolic Data Analysis: A Factorial Approach Based on Fuzzy Coded Data Chapter 17: Group Average Linkage Compared to Ward's Method in Hierarchical ClusteringChapter 18: Analysing a Pair of Tables: Coinertia Analysis and Duality Diagrams; References; Back Cover |
| Record Nr. | UNINA-9910799990403321 |
| Boca Raton : , : CRC Press, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Visualization and verbalization of data / / edited by Jorg Blasius, University of Bonn, Germany, Michael Greenacre, Universitat Pompeu Fabra, Barcelona, Spain
| Visualization and verbalization of data / / edited by Jorg Blasius, University of Bonn, Germany, Michael Greenacre, Universitat Pompeu Fabra, Barcelona, Spain |
| Pubbl/distr/stampa | Boca Raton : , : CRC Press, , [2014] |
| Descrizione fisica | 1 online resource (382 p.) |
| Disciplina | 001.4/226 |
| Collana | Chapman and Hall/CRC Computer Science and Data Analysis |
| Soggetto topico |
Information visualization
Correspondence analysis (Statistics) Multiple comparisons (Statistics) |
| ISBN |
0-429-16798-9
1-4665-8981-7 |
| Classificazione | MAT029000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Foreword; Preface; Editors; Contributors; Prologue: Let the Data Speak!; Chapter 1: Some Prehistory of CARME: Visual Language and Visual Thinking; Chapter 2: Some History of Algebraic Canonical Forms and Data Analysis; Chapter 3: Historical Elements of Correspondence Analysis and Multiple Correspondence Analysis; Chapter 4: History of Nonlinear Principal Component Analysis; Chapter 5: History of Canonical Correspondence Analysis; Chapter 6: History of Multiway Component Analysis and Three-Way Correspondence Analysis
Chapter 7: Past, Present, and Future of Multidimensional ScalingChapter 8: History of Cluster Analysis; Chapter 9: Simple Correspondence Analysis; Chapter 10: Distributional Equivalence and Linguistics; Chapter 11: Multiple Correspondence Analysis; Chapter 12: Structured Data Analysis; Chapter 13: Empirical Construction of Bourdieu's Social Space; Chapter 14: Multiple Factor Analysis:General Presentation and Comparison with STATIS; Chapter 15: Data Doubling and Fuzzy Coding; Chapter 16: Symbolic Data Analysis: A Factorial Approach Based on Fuzzy Coded Data Chapter 17: Group Average Linkage Compared to Ward's Method in Hierarchical ClusteringChapter 18: Analysing a Pair of Tables: Coinertia Analysis and Duality Diagrams; References; Back Cover |
| Record Nr. | UNINA-9910812112303321 |
| Boca Raton : , : CRC Press, , [2014] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
