Introduction to Multivariate Statistical Analysis

THEODORE W. ANDERSON, Professor Emeritus of Statistics and Economics at Stanford University, earned his PhD in mathematics at Princeton University. He is the author of The Statistical Analysis of Time Series, published by Wiley, as well as The New Statistical Analysis of Data and A Bibliography of Multivariate Statistical Analysis. Anderson is a member of the National Academy of Sciences and a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, the Econometric Society, and the American Academy of Arts and Sciences.

"…suitable for a graduate-level course on multivariate analysis…an important reference on the bookshelves of many scientific researchers and most practicing statisticians." (Journal of the American Statistical Association, September 2004) “…really well written. The edition will be certainly welcomed…” (Zentralblatt Math, Vo.1039, No.08, 2004)"…a wonderful textbook…that covers the mathematical theory of multivariate statistical analysis…" (Clinical Chemistry, Vol. 50, No. 2, May 2004)"...remains an authoritative work that can still be highly recommended..." (Short Book Reviews, 2004)"...still a very serious and comprehensive book on the statistical theory of multivariate analysis." (Technometrics, Vol. 46, No. 1, February 2004)“...remains a mathematically rigorous development of statistical methods for observations consisting of several measurements or characteristics of each subject and a study of their properties.” (Quarterly of Applied Mathematics, Vol. LXI, No. 4, December 2003)

• Preface to the Third Edition. 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 T2-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 Variance9. 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.