Methods for statistical data analysis of multivariate observations. second edition.

GNANADESIKAN, R.

ISBN 10: 0471161195 / ISBN 13: 9780471161196

Published by Wiley, New York, 1997

About this Item

24 cm. original hardcover. 354 pp. diagrams. references. index. "Wiley Series in probability and statistics". -very good. 730g.

Seller Inventory # 71486

Bibliographic Details

• Title: Methods for statistical data analysis of multivariate observations. second edition.
• Publisher: Wiley, New York
• Publication Date: 1997
• Language: English
• ISBN 10: 0471161195
• ISBN 13: 9780471161196
• Binding: Hardcover
• Edition: 2nd Edition

About this title

Synopsis

A practical guide for multivariate statistical techniques-- nowupdated and revised

In recent years, innovations in computer technology and statisticalmethodologies have dramatically altered the landscape ofmultivariate data analysis. This new edition of Methods forStatistical Data Analysis of Multivariate Observations explorescurrent multivariate concepts and techniques while retaining thesame practical focus of its predecessor. It integrates methods anddata-based interpretations relevant to multivariate analysis in away that addresses real-world problems arising in many areas ofinterest.

Greatly revised and updated, this Second Edition provides helpfulexamples, graphical orientation, numerous illustrations, and anappendix detailing statistical software, including the S (or Splus)and SAS systems. It also offers
* An expanded chapter on cluster analysis that covers advances inpattern recognition
* New sections on inputs to clustering algorithms and aids forinterpreting the results of cluster analysis
* An exploration of some new techniques of summarization andexposure
* New graphical methods for assessing the separations among theeigenvalues of a correlation matrix and for comparing sets ofeigenvectors
* Knowledge gained from advances in robust estimation anddistributional models that are slightly broader than themultivariate normal

This Second Edition is invaluable for graduate students, appliedstatisticians, engineers, and scientists wishing to usemultivariate techniques in a variety of disciplines.

About the Author

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.