Principal Component Analysis

Front Cover
Springer Science & Business Media, 2002 - Mathematics - 487 pages
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.
 

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Contents

Principal Components 2 2 Geometric Properties of Population Principal Components
2
Graphical Representation of Data Using
78
2
85
5
99
6
105
Choosing a Subset of Principal Components or Variables
111
Principal Component Analysis and Factor Analysis
150
Principal Components in Regression Analysis
167

Principal Components Used with Other Multivariate
199
Outlier Detection Influential Observations and Robust Estimation
232
Detection of Outliers Using Principal Components 10 1 1 Examples 10 2 Influential Observations in a Principal Component Analysis 10 2 1 Example...
269
Rotation of Principal Components 11 1 1 Examples 11 1 2 Onestep Procedures Using Simplicity Criteria 11 2 Alternatives to Rotation 11 2 1 Compo...
300
Principal Component Analysis for Special Types of Data
338
Generalizations and Adaptations of Principal
373
A Computation of Principal Components
406
63
418
64
424
71
435
200
473
308
476
316
478
222
479
400
480
333
481
335
482
225
486
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About the author (2002)

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

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