An Introduction to Multivariate Statistical Analysis 3rd Edition by Theodore W. Anderson | (PDF) Free Download

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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.

User’s Reviews

Editorial Reviews: Review “…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) From the Inside Flap 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 distributionsIncorporation 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 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 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 distributionsIncorporation 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. About the Author 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. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐What can U say, but Anderson is one of a couple of authors to have written seminal text on multivariate statistical analysis. One should have a background in univariate statistical analysis (e.g., Hogg, or possibly Rao). Although Anderson reviews matrix theory, at least a university level course is required. Required background also includes at least a full series of university-level calculus – multivariable and advanced – some complex variable theory would also be helpful as well as familiarity with advanced functions (e.g., Arfken). Also, it would be helpful to either read and/or have had a course in applied multivariable statistics (e.g. Johnson).Pros:It reviews both matrix algebra for statistics and univariate & linear models.The text is thorough in the topics it covers.It is terse in some of in its derivations – if that is your preference.Anderson’s MO is an excellent balance of both mathematical rigor, but not as stifling/limited as the standard Theorem-Lemma-Corollary, etc., format.Cons:It is lacking on examples and applications.This addition de-emphases some of the more modern Vec/Vech-matrix calculus that is more common in, say, modern econometrics.Many of the derivations would be easier with a background in Fourier analysis and KL/orthogonal-function expansions.The tables are a bit lame.Some theorem derivations would benefit from a tensorial representation.

⭐I wanted to know some mathematical details from multivariate analysis. Many books on multivariate analysis are written for those who are happy to skip all these details – and often, I too am in this category. But this time I wanted proofs and mathematical explanations. I found what I wanted in Anderson’s classic text. It is a masterly work of scholarship. The author is an authority on the subject; his writing is clear – if you like reading mathematics; the proofs are there; the text contains several hundred exercises – and they are not all research level exercises; hardly any typographical errors as far as I can see. A reasonable background in linear algebra, multivariate calculus and mathematical statistics will be helpful in reading this book. I have been around long enough not to read too much into the word “introduction” used in the title. The book will not help you to learn how to use computer packages for multivariate analysis; this is a book about mathematics.

⭐The paperback is exactly the same as the hardcover. It’s the international students edition for developing countries (Southasian version). And it’s way much cheaper!!It’s a very classic and great textbook and reference for multivariate statistical analysis. Many professors actually recommended us to read this book (computer science and biomedical/electrical engineering, data science labs).Now I don’t have to wait for the library copy every semester!! Love it!!

⭐The book is great, well printed and adequate details provided regarding the derivation of axioms, though there are two pits on the bottom of the back cover which are probably the results of careless delivery.

⭐it is real readable book for studying multivariate analysis.

⭐A great book.

⭐Great text!! Thanks, Tony Foley

⭐The first edition of Ted Anderson’s text on multivariate analysis was published in 1959. At the time it had no rivals. This book gives a thorough mathematical treatment of classical multivariate analysis. It is extremely well organized. Development of the multivariate normal distribution and its properties are given a thorough and rigorous treatment. The Wishart distribution is derived. Properties of the multivariate normal distribution are applied to problems of classification, principal components, canonical correlation and tests of hypotheses including the use of Hotelling’s T square.As a graduate student at Stanford, I audited Ted Anderson’s multivariate analysis course, that he taught out of the first edition of the book. It wasn’t until 1984 that he revised the text incorporating some new materials including the bootstrap method.This is an advanced course for graduate students in statistics. It is the best source for a rigorous mathematical treatment of the important results from the theory of the multivariate normal distribution. However, it is not easy reading for someone who is interested in applications but does not have strong training in mathematics (particularly linear algebra). For applications and approaches when the normal theory doesn’t apply, the book by Gnanadesikan is very good. There are now many good theoretical and applied texts on multivariate analysis including the text by Eaton, the one by Srivastava and Khatri, one by Rencher, one by Johnson and Wichern, and the one by Mardia, Kent and Bibby. Naik and Khattree have written a very nice applied multivariate book that demonstrates the applications using SAS software every step of the way.There are now many subspecialties including cluster analysis, principal components, correspondence analysis, factor analysis and classification that have complete texts devoted to them.Anderson has now published a third edition to this book and it incorporates bootstrap methods

⭐It is one of the best books available contentwise. That is why I am giving two stars. Otherwise, the print quality and the paper quality are so bad that the book would be worth zero stars. Pages are extremely thin, you can see one side from the other. The ink has bled while printing. Reading such poor quality books is not easy.

⭐Best book

⭐E’ un libro fondamentale per la comprensione della multivariate analysis.I am happy with the Book.

⭐Nice

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