Methods for Statistical Data Analysis of Multivariate Observations

Gnanadesikan R.

ISBN 10: 0471308455 / ISBN 13: 9780471308454

Edité par Wiley, New York, 1977

A propos de cet article

dustwrapper worn, text clean - no student markings in text. Size: 8vo - over 7¾" - 9¾" tall.

N° de réf. du vendeur 21113

Détails bibliographiques

• Titre: Methods for Statistical Data Analysis of Multivariate Observations
• Éditeur: Wiley, New York
• Date de parution: 1977
• Langue: anglais
• ISBN à 10 chiffres: 0471308455
• ISBN à 13 chiffres: 9780471308454
• Reliure: Hard Cover
• Édition: First Edition.
• État de l'article: Very Good
• État de la jaquette: Good
• Catalogues du vendeur:
  • Sciences and Technology

A propos de ce titre

Synopsis

Book by Gnanadesikan Ram

Présentation de l'éditeur

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.