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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    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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