An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics) - Hardcover

Anderson, T. W.

 
9780471889878: An Introduction to Multivariate Statistical Analysis (Wiley Series in Probability and Statistics)
Hardcover
ISBN 10: 0471889873 ISBN 13: 9780471889878
Publisher: Wiley–Blackwell, 1984

Synopsis

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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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.

From the Back Cover

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.

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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Publisher
Wiley–Blackwell
Publication date
1984
Language
English
ISBN 10 
0471889873
ISBN 13 
9780471889878
Binding
Hardcover
Edition number
2
Number of pages
704