Item Details

# Multivariate Data Reduction and Discrimination With SAS Software

Ravindra Khattree, Dayanand N. Naik
Format
Book
Published
Cary, N.C. : SAS Institute Inc., 2000.
Language
English
ISBN
0471323004 (pbk. : acid-free paper)
Contents
• Commonly Used Notation xiii
• 1 Basic Concepts for Multivariate Statistics 1
• 1.2 Population Versus Sample 2
• 1.3 Elementary Tools for Understanding Multivariate Data 3
• 1.4 Data Reduction, Description, and Estimation 6
• 1.5 Concepts from Matrix Algebra 7
• 1.6 Multivariate Normal Distribution 21
• 2 Principal Component Analysis 25
• 2.2 Population Principal Components 26
• 2.3 Sample Principal Components 29
• 2.4 Selection of the Number of Principal Components 40
• 2.5 Some Applications of Principal Component Analysis 46
• 2.6 Principal Component Analysis of Compositional Data 57
• 2.7 Principal Component Regression 60
• 2.8 Principal Component Residuals and Detection of Outliers 65
• 2.9 Principal Component Biplot 69
• 2.10 PCA Using SAS/INSIGHT Software 76
• 3 Canonical Correlation Analysis 77
• 3.2 Population Canonical Correlations and Canonical Variables 78
• 3.3 Sample Canonical Correlations and Canonical Variables 79
• 3.4 Canonical Analysis of Residuals 91
• 3.5 Partial Canonical Correlations 92
• 3.6 Canonical Redundancy Analysis 95
• 3.7 Canonical Correlation Analysis of Qualitative Data 101
• 3.8 'Partial Tests' in Multivariate Regression 106
• 4 Factor Analysis 111
• 4.2 Factor Model 112
• 4.3 A Difference between PCA and Factor Analysis 116
• 4.4 Noniterative Methods of Estimation 118
• 4.5 Iterative Methods of Estimation 139
• 4.6 Heywood Cases 155
• 4.7 Comparison of the Methods 156
• 4.8 Factor Rotation 158
• 4.9 Estimation of Factor Scores 177
• 4.10 Factor Analysis Using Residuals 184
• 4.11 Some Applications 188
• 5 Discriminant Analysis 211
• 5.2 Multivariate Normality 212
• 5.3 Statistical Tests for Relevance 231
• 5.4 Discriminant Analysis: Fisher's Approach 242
• 5.5 Discriminant Analysis for k Normal Populations 255
• 5.6 Canonical Discriminant Analysis 282
• 5.7 Variable Selection in Discriminant Analysis 296
• 5.8 When Dimensionality Exceeds Sample Size 304
• 5.9 Logistic Discrimination 314
• 5.10 Nonparametric Discrimination 333
• 6 Cluster Analysis 347
• 6.2 Graphical Methods for Clustering 348
• 6.3 Similarity and Dissimilarity Measures 356
• 6.4 Hierarchical Clustering Methods 359
• 6.5 Clustering of Variables 380
• 6.6 Nonhierarchical Clustering: k-Means Approach 393
• 6.7 How Many Clusters: Cubic Clustering Criterion 421
• 6.8 Clustering Using Density Estimation 427
• 6.9 Clustering with Binary Data 435
• 7 Correspondence Analysis 443
• 7.2 Correspondence Analysis 444
• 7.3 Multiple Correspondence Analysis 463
• 7.4 CA as a Canonical Correlation Analysis 476
• 7.5 Correspondence Analysis Using Andrews Plots 479
• 7.6 Correspondence Analysis Using Hellinger Distance 490
• 7.7 Canonical Correspondence Analysis 498.
Description
xiv, 558 p. : ill. ; 28 cm.
Notes
Includes bibliographical references (p. [535]-542) and index.
Technical Details
• Access in Virgo Classic

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a| Multivariate data reduction and discrimination with SAS software / c| Ravindra Khattree, Dayanand N. Naik.
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a| xiv, 558 p. : b| ill. ; c| 28 cm.
a| Includes bibliographical references (p. [535]-542) and index.
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t| Commonly Used Notation g| xiii -- g| 1 t| Basic Concepts for Multivariate Statistics g| 1 -- g| 1.2 t| Population Versus Sample g| 2 -- g| 1.3 t| Elementary Tools for Understanding Multivariate Data g| 3 -- g| 1.4 t| Data Reduction, Description, and Estimation g| 6 -- g| 1.5 t| Concepts from Matrix Algebra g| 7 -- g| 1.6 t| Multivariate Normal Distribution g| 21 -- g| 2 t| Principal Component Analysis g| 25 -- g| 2.2 t| Population Principal Components g| 26 -- g| 2.3 t| Sample Principal Components g| 29 -- g| 2.4 t| Selection of the Number of Principal Components g| 40 -- g| 2.5 t| Some Applications of Principal Component Analysis g| 46 -- g| 2.6 t| Principal Component Analysis of Compositional Data g| 57 -- g| 2.7 t| Principal Component Regression g| 60 -- g| 2.8 t| Principal Component Residuals and Detection of Outliers g| 65 -- g| 2.9 t| Principal Component Biplot g| 69 -- g| 2.10 t| PCA Using SAS/INSIGHT Software g| 76 -- g| 3 t| Canonical Correlation Analysis g| 77 -- g| 3.2 t| Population Canonical Correlations and Canonical Variables g| 78 -- g| 3.3 t| Sample Canonical Correlations and Canonical Variables g| 79 -- g| 3.4 t| Canonical Analysis of Residuals g| 91 -- g| 3.5 t| Partial Canonical Correlations g| 92 -- g| 3.6 t| Canonical Redundancy Analysis g| 95 -- g| 3.7 t| Canonical Correlation Analysis of Qualitative Data g| 101 -- g| 3.8 t| 'Partial Tests' in Multivariate Regression g| 106 -- g| 4 t| Factor Analysis g| 111 -- g| 4.2 t| Factor Model g| 112 -- g| 4.3 t| A Difference between PCA and Factor Analysis g| 116 -- g| 4.4 t| Noniterative Methods of Estimation g| 118 -- g| 4.5 t| Iterative Methods of Estimation g| 139 -- g| 4.6 t| Heywood Cases g| 155 -- g| 4.7 t| Comparison of the Methods g| 156 -- g| 4.8 t| Factor Rotation g| 158 -- g| 4.9 t| Estimation of Factor Scores g| 177 -- g| 4.10 t| Factor Analysis Using Residuals g| 184 -- g| 4.11 t| Some Applications g| 188 -- g| 5 t| Discriminant Analysis g| 211 -- g| 5.2 t| Multivariate Normality g| 212 -- g| 5.3 t| Statistical Tests for Relevance g| 231 -- g| 5.4 t| Discriminant Analysis: Fisher's Approach g| 242 -- g| 5.5 t| Discriminant Analysis for k Normal Populations g| 255 -- g| 5.6 t| Canonical Discriminant Analysis g| 282 -- g| 5.7 t| Variable Selection in Discriminant Analysis g| 296 -- g| 5.8 t| When Dimensionality Exceeds Sample Size g| 304 -- g| 5.9 t| Logistic Discrimination g| 314 -- g| 5.10 t| Nonparametric Discrimination g| 333 -- g| 6 t| Cluster Analysis g| 347 -- g| 6.2 t| Graphical Methods for Clustering g| 348 -- g| 6.3 t| Similarity and Dissimilarity Measures g| 356 -- g| 6.4 t| Hierarchical Clustering Methods g| 359 -- g| 6.5 t| Clustering of Variables g| 380 -- g| 6.6 t| Nonhierarchical Clustering: k-Means Approach g| 393 -- g| 6.7 t| How Many Clusters: Cubic Clustering Criterion g| 421 -- g| 6.8 t| Clustering Using Density Estimation g| 427 -- g| 6.9 t| Clustering with Binary Data g| 435 -- g| 7 t| Correspondence Analysis g| 443 -- g| 7.2 t| Correspondence Analysis g| 444 -- g| 7.3 t| Multiple Correspondence Analysis g| 463 -- g| 7.4 t| CA as a Canonical Correlation Analysis g| 476 -- g| 7.5 t| Correspondence Analysis Using Andrews Plots g| 479 -- g| 7.6 t| Correspondence Analysis Using Hellinger Distance g| 490 -- g| 7.7 t| Canonical Correspondence Analysis g| 498.
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a| Naik, Dayanand N.
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