An Introduction to Multivariate Statistical Analysis

ISBN-13: 9780471360919 / Angielski / Twarda / 2003 / 752 str.

T. W. Anderson

Opis

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.

Recenzje

" 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)

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.

O autorze

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.

Blurb

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 chapter
• Measures of correlation and tests of independence
• Reduced rank regression, including the limited–information maximum–likelihood estimator of an equation in a simultaneous equations model
• Elliptically 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.