Principal Component Analysis

Ian Jolliffe - Collection Springer Series In Statistics

502 pages, parution le 06/11/2002 (2^eme édition)

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Résumé

Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques it continues to be the subject of much research, ranging from new model- based approaches to algorithmic ideas from neural networks. It is extremely versatile with applications in many disciplines.

The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition.

Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra.

L'auteur - Ian Jolliffe

Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years.

Sommaire

Introduction
Properties of Population Principal Components
Properties of Sample Principal Components
Interpreting Principal Components: Examples
Graphical Representation of Data Using Principal Components
Choosing a Subset of Principal Components or Variables
Principal Component Analysis and Factor Analysis
Principal Components in Regression Analysis
Principal Components Used with Other Multivariate Techniques
Outlier Detection, Influential Observations and Robust Estimation
Rotation and Interpretation of Principal Components
Principal Component Analysis for Time Series and Other Non-Independent Data
Principal Component Analysis for Special Types of Data
Generalizations and Adaptations of Principal Component Analysis

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Ian Jolliffe
Collection	Springer Series In Statistics
Parution	06/11/2002
Édition	2^eme édition
Nb. de pages	502
Format	15,5 x 24
Couverture	Relié
Poids	875g
Intérieur	Noir et Blanc
EAN13	9780387954424
ISBN13	978-0-387-95442-4