An Introduction to Multivariate Statistical Analysis

A classic comprehensive sourcebook, now fully updated
For more than four decades An Introduction to Multivariate Statistical Analysis has been an invaluable text for students and a resource for professionals wishing to acquire a basic knowledge of multivariate statistical analysis. Since the previous edition, the field has grown significantly. This updated and improved Third Edition familiarizes readers with these new advances, elucidating several aspects that are particularly relevant to methodology and comprehension.
The Third Edition features new or more extensive coverage of: Patterns of Dependence and Graphical Models–a new chapterMeasures of correlation and tests of independenceReduced rank regression, including the limited–information maximum–likelihood estimator of an equation in a simultaneous equations modelElliptically contoured distributions
Incorporation of the advice and comments of the readers of the first two editions as well as extensively classroom–tested techniques and calculations makes An Introduction to Multivariate Statistical Analysis, Third Edition, more valuable than ever for both professional statisticians and students of multivariate statistics.

Spis treści:
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 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.

Nota biograficzna:
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.

Okładka tylna:
A classic comprehensive sourcebook, now fully updated
For more than four decades An Introduction to Multivariate Statistical Analysis has been an invaluable text for students and a resource for professionals wishing to acquire a basic knowledge of multivariate statistical analysis. Since the previous edition, the field has grown significantly. This updated and improved Third Edition familiarizes readers with these new advances, elucidating several aspects that are particularly relevant to methodology and comprehension.
The Third Edition features new or more extensive coverage of: Patterns of Dependence and Graphical Models–a new chapterMeasures of correlation and tests of independenceReduced rank regression, including the limited–information maximum–likelihood estimator of an equation in a simultaneous equations modelElliptically contoured distributions
Incorporation of the advice and comments of the readers of the first two editions a s well as extensively classroom–tested techniques and calculations makes An Introduction to Multivariate Statistical Analysis, Third Edition, more valuable than ever for both professional statisticians and students of multivariate statistics.