An Introduction to Multivariate Statistical Analysis, 2nd Edition

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9780471889878: An Introduction to Multivariate Statistical Analysis, 2nd Edition
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Multivariate Statistical Simulation Mark E. Johnson For the researcher in statistics, probability, and operations research involved in the design and execution of a computer-aided simulation study utilizing continuous multivariate distributions, this book considers the properties of such distributions from a unique perspective. With enhancing graphics (three-dimensional and contour plots), it presents generation algorithms revealing features of the distribution undisclosed in preliminary algebraic manipulations. Well-known multivariate distributions covered include normal mixtures, elliptically assymmetric, Johnson translation, Khintine, and Burr-Pareto-logistic. 1987 (0 471-82290-6) 230 pp. Aspects of Multivariate Statistical Theory Robb J. Muirhead A classical mathematical treatment of the techniques, distributions, and inferences based on the multivariate normal distributions. The main focus is on distribution theory—both exact and asymptotic. Introduces three main areas of current activity overlooked or inadequately covered in existing texts: 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. 1982 (0 471-09442-0) 673 pp. Multivariate Observations G. A. F. Seber This up-to-date, comprehensive sourcebook treats data-oriented techniques and classical methods. It concerns the external analysis of differences among objects, and the internal analysis of how the variables measured relate to one another within objects. The scope ranges from the practical problems of graphically representing high dimensional data to the theoretical problems relating to matrices of random variables. 1984 (0 471-88104-X) 686 pp.

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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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