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Informationen zum Autor GEORGE A. F. SEBER is a Professor in the Department of Statistics at The University of Auckland in New Zealand. Klappentext WILEY-INTERSCIENCE PAPERBACK SERIESThe 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."In recent years many monographs have been published on specialized aspects of multivariate data-analysis-on cluster analysis, multidimensional scaling, correspondence analysis, developments of discriminant analysis, graphical methods, classification, and so on. This book is an attempt to review these newer methods together with the classical theory. . . . This one merits two cheers."-J. C. Gower, Department of StatisticsRothamsted Experimental Station, Harpenden, U.K.Review in Biometrics, June 1987Multivariate Observations is a comprehensive sourcebook that treats data-oriented techniques as well as classical methods. Emphasis is on principles rather than mathematical detail, and coverage ranges from the practical problems of graphically representing high-dimensional data to the theoretical problems relating to matrices of random variables. Each chapter serves as a self-contained survey of a specific topic. The book includes many numerical examples and over 1,100 references. Zusammenfassung This up-to-date, comprehensive sourcebook treats data-oriented techniques as well as classical methods. Emphasis is on principles rather than mathematical detail, and coverage ranges from the practical problems of graphically representing high dimensional data to the theoretical problems relating to matrices of random variables. Each chapter serves as a self-contained survey of a specific topic. Includes many numerical examples, and over 1,100 references. Inhaltsverzeichnis Notation. 1. Preliminaries. 2. Multivariate Distributions. 3. Inference for the Multivariate Normal. 4. Graphical and Data-Oriented Techniques. 5. Dimension Reduction and Ordination. 6. Discriminant Analysis. 7. Cluster Analysis. 8. Multivariate Linear Models. 9. Multivariate Analysis of Variance and Covariance. 10. Special Topics. Appendix A: Some Matrix Algebra. Appendix B: Orthogonal Projections. Appendix C: Order Statistics and Probability Plotting. Appendix D: Statistical Tables. Outline Solutions to Exercises. References. Index. ...
