Methods for Statistical Data Analysis of Multivariate Observations |
|
| Author:
| Gnanadesikan, R. |
| Series title: | Wiley Series in Probability and Statistics Ser. |
| ISBN: | 978-1-118-03092-9 |
| Publication Date: | Jan 2011 |
| Publisher: | John Wiley & Sons, Incorporated
|
| Imprint: | Wiley-Interscience |
| Book Format: | Digital download |
| List Price: | Contact Supplier contact
Contact Supplier contact
|
Book Description:
|
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...
More DescriptionA 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.