Synopses & Reviews
Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications.
Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; 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, with particular focus on multidimensional scaling and clustering.
In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.
Synopsis
Suitable for upper-level undergraduates and graduate students, this two-part treatment covers the foundations of multivariate analysis as well as models and applications.
Synopsis
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.
Table of Contents
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