# An Introduction to Multivariate Statistical Analysis

## Theodore W. Anderson

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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.

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From the Back Cover:

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.

From the Inside Flap:

An Introduction to Multivariate Statistical Analysis, 2nd Edition is a major updating of a work widely regarded as the standard, authoritative text in the field. It provides students and practicing statisticians with the latest theory and methods, plus the most important developments that have occurred over the past 25 years. As in the first edition, the text provides a mathematically rigorous development of the statistical methods used to analyze multivariate data. While maximum likelihood estimators have been principal tools of multivariate statistical analysis, this book introduces alternatives that are better suited for certain loss functions, such as Stein and Bayes estimators. Likelihood ratio tests have been supplemented by other invariant procedures. New results on distributions are given and some significance points are tabulated. Properties of these procedures such as power functions, admissibility, unbiasedness, and monotonicity of power functions are covered, and simultaneous confidence intervals for means and covariances are studied. Other new topics introduced in this edition include simultaneous equations models and linear functional relationships, with 50% more problems than in the previous edition.

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## 2.An Introduction to Multivariate Statistical Analysis, 3rd ed.

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## 3.An Introduction to Multivariate Statistical Analysis

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