An Introduction to Multivariate Statistical Analysis
Wiley Series in Probability and Statistics
3. Edition August 2003
752 Pages, Hardcover
Wiley & Sons Ltd
Short Description
The Second Edition of An Introduction to Multivariate Statistical Analysis has become a standard in the field. Since its publication, several advances have been made in multivariate (MV) statistical analysis. Maintaining the previous edition's mathematically rigorous development of statistical models, the Third Edition substantially revises and adds to the original text to bring statisticians up to date with recent developments in the field.
Perfected over three editions and more than forty years, this field- and classroom-tested reference:
* Uses the method of maximum likelihood to a large extent to ensure reasonable, and in some cases optimal procedures.
* Treats all the basic and important topics in multivariate statistics.
* Adds two new chapters, along with a number of new sections.
* Provides the most methodical, up-to-date information on MV statistics available.
Preface to the Second Edition.
Preface to the First Edition.
1. Introduction.
2. The Multivariate Normal Distribution.
3. Estimation of the Mean Vector and the Covariance Matrix.
4. The Distributions and Uses of Sample Correlation Coefficients.
5. The Generalized T^2-Statistic.
6. Classification of Observations.
7. The Distribution of the Sample Covariance Matrix and the Sample Generalized Variance.
8. Testing the General Linear Hypothesis: Multivariate Analysis of Variance
9. Testing Independence of Sets of Variates.
10. Testing Hypotheses of Equality of Covariance Matrices and Equality of Mean Vectors and Covariance Matrices.
11. Principal Components.
12. Cononical Correlations and Cononical Variables.
13. The Distributions of Characteristic Roots and Vectors.
14. Factor Analysis.
15. Pattern of Dependence; Graphical Models.
Appendix A: Matrix Theory.
Appendix B: Tables.
References.
Index.
