Synopses & Reviews
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 theoryboth 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.
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)
Synopsis
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.
Synopsis
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 Modelsa 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.
Synopsis
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.
About the Author
About the author Theodore W. Anderson is Professor of Statistics and Economics at Stanford University. He is the author of The Statistical Analysis of Time Series, A Bibliography of Multivariate Statistical Analysis, and An Introduction to the Statistical Analysis of Data. Dr. Anderson is a Fellow of the Institute of Mathematical Statistics, the American Statistical Association, the Royal Statistical Society, and the American Academy of Arts & Sciences. He is a member of the American Mathematical Society, the International Institute of Statistics, and the National Academy of Sciences. Dr. Anderson earned his PhD in mathematics at Princeton University.
Table of Contents
The Multivariate Normal Distribution.
Estimation of the Mean Vector and the Covariance Matrix.
The Distributions and Uses of Sample Correlation Coefficients.
The Generalized T2-Statistic.
Classification of Observations.
The Distribution of the Sample Covariance Matrix and the Sample Generalized Variance.
Testing the General Linear Hypothesis: Multivariate Analysis of Variance.
Testing Independence of Sets of Variates.
Testing Hypotheses of Equality of Covariance Matrices and Equality of Mean Vectors and Covariance Matrices.
Principal Components.
Canonical Correlations and Canonical Variables.
The Distributions of Characteristic Roots and Vectors.
Factor Analysis.
Appendixes.
References.
Index.