Multivariate Statistics: Old School is a
mathematical and methodological introduction to multivariate statistical analysis.
It presents the basic mathematical grounding that graduate statistics students need for future research, and
important multivariate techniques useful to statisticians in general.
This text was developed over many years by the author,
John Marden,
while teaching in the Department of Statistics, University of Illinois
at Urbana-Champaign.
Topics include
- The multivariate normal and Wishart distributions
- Linear models, including multivariate regression and analysis of variance, and both-sides models (GMANOVA, repeated measures, growth curves)
- Linear algebra useful for multivariate statistics
- Covariance structures, including principal components, factor analysis, independence and conditional independence, and symmetry models
- Classification (linear and quadratic discrimination, trees, logistic regression)
- Clustering (K-means, model-based, hierarchical)
- Other techniques, including biplots, canonical correlations, and multidimensional scaling
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