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Other titles in the Wiley Series in Probability and Statistics series:
An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics)by T. W. Anderson
Synopses & Reviews
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:
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.
Book News Annotation:
Aimed at professional statisticians as well as graduate students, this text presents an introduction to multivariate statistical analysis. Anderson (emeritus, statistics and economics, Stanford U.) discusses such topics as the estimation of the mean vector and the covariance matrix, the classification of observations, and the distributions of characteristic roots and vectors. Updated to reflect recent advances in the field (the first edition was published in 1957), the third edition features expanded information on a number of topics, including a new chapter on patterns of dependence and graphical models. Annotation (c)2003 Book News, Inc., Portland, OR (booknews.com)
Includes bibliographical references (p. 687-711) and index.
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.
About the Author
"…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)
Table of Contents
Preface to the Third Edition.
Preface to the Second Edition.
Preface to the First Edition.
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.
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