Contents
Chapter 1. Basic Concepts for Multivariate Statistics
Chapter 2. Principal Component Analysis
Chapter 3. Canonical Correlation Analysis
Chapter 4. Factor Analysis
Chapter 5. Discriminant Analysis
Chapter 6. Cluster Analysis
Chapter 7. Correspondence Analysis
Appendix: Data Sets
References
Index
Chapter 1: Basic Concepts for Multivariate Statistics
- Introduction
- Population Versus Sample
- Elementary Tools for Understanding Multivariate Data
- Data Reduction, Description, and Estimation
- Concepts from Matrix Algebra
- Multivariate Normal Distribution
- Concluding Remarks
Chapter 2: Principal Component Analysis
- Introduction
- Population Principal Components
- Sample Principal Components
- Selection of the Number of Principal Components
- Some Applications of Principal Component Analysis
- Principal Component Analysis of Compositional Data
- Principal Component Regression
- Principal Component Residual and Detention of Outliner
- Principal Component Biplot
- PCA Using SAS/INSIGHT Software
- Concluding Remarks
Chapter 3: Canonical Correlation Analysis
- Introduction
- Population Canonical Correlations and Canonical Variables
- Sample Canonical Cprrelations and Canonical Variables
- Canonical Analysis of Residuals
- Partial Canonical Correlations
- Canonical Redundancy Analysis
- Canonical Correlation Analysis of Qualitative Data
Chapter 4: Factor Analysis
- Introduction
- Factor Model
- A Difference between PCA and Factor Analysis
- Npniterative Methods of Estimation
- Iterative Methods of Estimation
- Heywood Cases
- Comparison of the Methods
- Factor Rotation
- Estimation of Factor Scores
- Factor Analysis Using Residuals
- Some Applications
- Concluding Remarks
Chapter 5: Discriminant Analysis
- Introduction
- Multiviariate Normality
- Statistical Tests for Relevance
- Discriminant Analysis: Fisher's Approach
- Discriminant Analysis for k Normal Population
- Canonical Discriminant Analysis
- Variable Selection in Discriminant Analysis
- When Dimensionally Exceeds Sample Size
- Logistic Discrimination
- Nonparametric Discrimination
- Concluding Remarks
Chapter 6: Cluster Analysis
- Introduction
- Graphical Methods for Clustering
- Similarity and Dissimilarity Measures
- Hierarchical Clustering Methods
- Clustering of Variables
- Nonhierarchical Clustering: k-Means Approach
- How Many Clusters: Cubic Clustering Criterion
- Clustering Using Density Estimation
- Clustering with Binary Data
- Concluding Remarks
Chapter 7: Correspondence Analysis
- Introduction
- Correspondence Analysis
- Multiple Correspondence Analysis
- CA as a Canonical Correlation Analysis
- Correspondence Analysis Using Andrews Plots
- Canonical Correspondence Analysis Using Hellinger Distance
- Concluding Remarks
Appendix - Data Sets
References
Index