Applied Multivariate Analysis Using Bayesian and Frequentist Methods of Inference, Second Edition

Includes practical elements of matrix theory, continuous multivariate distributions and basic multivariate statistics in the normal distribution; regression and the analysis of variance; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations. 1982 edition.

Preface
Notation
I. Introduction
1. Foundations
2. Matrix Theory Useful in Multivariate Analysis
3. Continuous Multivariate Distributions, The Normal Distribution, Bayesian Inference
4. Multivariate Large Sample Distributions and Approximations
5. The Wishart and Related Distributions
6. Other Continuous Multivariate Distributions
7. Basic Multivariate Statistics in the Normal Distribution
II. Models
8. Regression and the Analysis of Variance
9. Principal Components
10. Factor Analysis and Latent Structure Analysis
11. Canonical Correlations
12. Stable Portfolio Analysis
13. Classifications and Discrimination Models
14. Control in the Multivariate Linear Model
15. Structuring Multivariate Populations (Multidimensional Scaling and Clustering)
Appendixes
Index