Principal Component Analysis

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 2002-10-01

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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. 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.

Author Biography

I. T. Jolliffe is Professor of Statistics at the University of Aberdeen.

Table of Contents

Preface to the Second Edition
Preface to the First Edition
List of Figures
List of Tables
Introductionp. 1
Properties of Population Principal Componentsp. 10
Properties of Sample Principal Componentsp. 29
Interpreting Principal Components: Examplesp. 63
Graphical Representation of Data Using Principal Componentsp. 78
Choosing a Subset of Principal Components or Variablesp. 111
Principal Component Analysis and Factor Analysisp. 150
Principal Components in Regression Analysisp. 167
Principal Components Used with Other Multivariate Techniquesp. 199
Outlier Detection, Influential Observations and Robust Estimationp. 232
Rotation and Interpretation of Principal Componentsp. 269
PCA for Time Series and Other Non-Independent Datap. 299
Principal Component Analysis for Special Types of Datap. 338
Generalizations and Adaptations of Principal Component Analysisp. 373
Computation of Principal Componentsp. 407
Indexp. 458
Author Indexp. 478
Table of Contents provided by Blackwell. All Rights Reserved.

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