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Aspects of Multivariate Statistical Theory (Wiley Series in Probability and Statistics)
 
 
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Aspects of Multivariate Statistical Theory (Wiley Series in Probability and Statistics) [Hardcover]

Robb J. Muirhead (Author)
5.0 out of 5 stars  See all reviews (2 customer reviews)


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Book Description

0471094420 978-0471094425 March 1982 1
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

". . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to penetrate deeper into the multivariate field."
-Mededelingen van het Wiskundig Genootschap

"This book is a comprehensive and clearly written text on multivariate analysis from a theoretical point of view."
-The Statistician

Aspects of Multivariate Statistical Theory presents a classical mathematical treatment of the techniques, distributions, and inferences based on multivariate normal distribution. Noncentral distribution theory, decision theoretic estimation of the parameters of a multivariate normal distribution, and the uses of spherical and elliptical distributions in multivariate analysis are introduced. Advances in multivariate analysis are discussed, including decision theory and robustness. The book also includes tables of percentage points of many of the standard likelihood statistics used in multivariate statistical procedures. This definitive resource provides in-depth discussion of the multivariate field and serves admirably as both a textbook and reference.


Editorial Reviews

From the Publisher

A classical mathematical treatment of the techniques, distributions, and inferences based on the multivariate normal distribution. Introduces noncentral distribution theory, decision theoretic estimation of the parameters of a multivariate normal distribution, and the uses of spherical and elliptical distributions in multivariate analysis. Discusses recent advances in multivariate analysis, including decision theory and robustness. Also includes tables of percentage points of many of the standard likelihood statistics used in multivariate statistical procedures.

From the Back Cover

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

". . . the wealth of material on statistics concerning the multivariate normal distribution is quite exceptional. As such it is a very useful source of information for the general statistician and a must for anyone wanting to penetrate deeper into the multivariate field."
—Mededelingen van het Wiskundig Genootschap

"This book is a comprehensive and clearly written text on multivariate analysis from a theoretical point of view."
—The Statistician

Aspects of Multivariate Statistical Theory presents a classical mathematical treatment of the techniques, distributions, and inferences based on multivariate normal distribution. Noncentral distribution theory, decision theoretic estimation of the parameters of a multivariate normal distribution, and the uses of spherical and elliptical distributions in multivariate analysis are introduced. Advances in multivariate analysis are discussed, including decision theory and robustness. The book also includes tables of percentage points of many of the standard likelihood statistics used in multivariate statistical procedures. This definitive resource provides in-depth discussion of the multivariate field and serves admirably as both a textbook and reference. --This text refers to the Paperback edition.


Product Details

  • Hardcover: 704 pages
  • Publisher: Wiley-Interscience; 1 edition (March 1982)
  • Language: English
  • ISBN-10: 0471094420
  • ISBN-13: 978-0471094425
  • Product Dimensions: 9.1 x 6.3 x 1.7 inches
  • Shipping Weight: 2.4 pounds
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Best Sellers Rank: #3,425,670 in Books (See Top 100 in Books)

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37 of 37 people found the following review helpful:
5.0 out of 5 stars excellent theoretical treatment of multivariate theory, May 19, 2008
Robb Muirhead takes a strong theoretical approach to multivariate analysis much like Anderson. But although his emphasis is on distribution theory he does not restrct himslef to the multivariate normal distribution. The text is one of the best in terms of exposition and is now considered a classic. Being over 20 years old it is missing some of the latest developments.
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23 of 24 people found the following review helpful:
5.0 out of 5 stars Best theory book on multivariate statistics, October 10, 2005
Multivariate statistics is probably one of the most applied, and at the same time, most theoretical aspect of Statistics. While there are various other books on this subject, this book gives, probably, one of the best, and most elegant account on the theory, particularly multivariate distribution theory, which is commonly encountered in applications. This book gives a definitive account of the techniques used to derive multivariate distributions, starting from R.A. Fisher, P L Hsu's work Pao-Lu Hsu: Collected Papers, to modern developments of A T James, and others. This book is a must-read for anyone who ventures into research in multivariate statistics. Thus, I'm glad to see a reprint paperback issue of the 1982 classic, and I wish someone will write an update in the future at the same theoretical depth and but maybe with more breadth.
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Inside This Book (learn more)
First Sentence:
The basic, central distribution and building block in classical multivariate analysis is the multivariate normal distribution. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
nonzero latent roots, sample canonical correlation coefficients, zonal polynomial expansion, modified likelihood ratio statistic, uniformly most powerful invariant test, zonal polynomials, noncentrality matrix, single classification model, same latent roots, multivariate beta distribution, sample multiple correlation coefficient, hth moment, smallest latent roots, invariant test statistics, multivariate gamma function, largest latent root, modified likelihood ratio test, monomial symmetric functions, symmetric homogeneous polynomial, asymptotic null distribution, maximal invariant, likelihood ratio criteria, elliptical distribution, normalized invariant measure, invariance point
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Das Gupta, Kronecker Products, Proof Let, Proof Put, Prove Lemma, Applying Lemma, Monte Carlo, Obtain Corollary, Proof From Theorem
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