Statistical Data Analysis 2E: 321 (Wiley Series in Probability and Statistics) - Hardcover

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R. GNANADESIKAN, PhD, is a professor in the Department of Statistics at Rutgers University. He received his doctorate from the University of North Carolina. A former chairperson of Section U of the American Statistical Association (ASA) and past president of the Institute of Mathematical Statistics, he is a fellow of the American Association for the Advancement of Science, the Royal Statistical Society, and the ASA. Professor Gnanadesikan is the author of more than 75 technical publications and author/editor of three previous books.

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A practical guide for multivariate statistical techniques— now updated and revised

In recent years, innovations in computer technology and statistical methodologies have dramatically altered the landscape of multivariate data analysis. This new edition of Methods for Statistical Data Analysis of Multivariate Observations explores current multivariate concepts and techniques while retaining the same practical focus of its predecessor. It integrates methods and data-based interpretations relevant to multivariate analysis in a way that addresses real-world problems arising in many areas of interest.

Greatly revised and updated, this Second Edition provides helpful examples, graphical orientation, numerous illustrations, and an appendix detailing statistical software, including the S (or Splus) and SAS systems. It also offers

• An expanded chapter on cluster analysis that covers advances in pattern recognition
• New sections on inputs to clustering algorithms and aids for interpreting the results of cluster analysis
• An exploration of some new techniques of summarization and exposure
• New graphical methods for assessing the separations among the eigenvalues of a correlation matrix and for comparing sets of eigenvectors
• Knowledge gained from advances in robust estimation and distributional models that are slightly broader than the multivariate normal

This Second Edition is invaluable for graduate students, applied statisticians, engineers, and scientists wishing to use multivariate techniques in a variety of disciplines.