This course covers methods for analyzing multivariate data.
1. Graphical methods
2. Modeling and inference using the multivariate normal distribution
- Multivariate data and models
- Multivariate Normal distribution
- Traditional inference: Multivariate Regression, MANOVA, etc.
- Links with mixed linear models and hierarchical modeling.
3. Variable selection – LARS, LASSO
4. Exploratory techniques based on eigenvalue and singular decomposition
- SVD of a data matrix; special decomposition
- Principal Component Analysis
- Factor Analysis
- Canonical Correlation
5. Classification and clustering
- Linear discrimination
- Classification trees
- Hierarchical clustering
6. K-means clustering
- Dimension reduction
- Multidimensional scaling
7. Correspondence analysis