Principal Component Analysis

Front Cover
Springer Science & Business Media, May 9, 2006 - Mathematics - 488 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

Introduction
1
Properties of Population Principal Components
10
Properties of Sample Principal Components
29
Examples
64
Graphical Representation of Data Using
78
6
111
Principal Component Analysis and Factor Analysis
150
Principal Components in Regression Analysis
167
Generalizations and Adaptations of Principal
373
33
397
A Computation of Principal Components
406
63
418
64
424
39
427
71
435
78
450

Principal Components Used with Other Multivariate
199
10
232
11
269
18
285
PCA for Time Series and Other NonIndependent Data
300
21
315
Functional
316
PCA and NonIndependent DataSome Additional Topics
328
Other Aspects of NonIndependent Data and
335
Principal Component Analysis for Special Types of Data
338
27
350
80
458
7
463
120
465
270
469
130
471
392
472
296
476
400
480
335
482
225
486
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