Principal Component Analysis 2nd edition by Jolliffe – Ebook PDF Instant Download/Delivery: 0387224404, 978-0387224404
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ISBN 10: 0387224404
ISBN 13: 978-0387224404
Author: Jolliffe
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.
Principal Component Analysis 2nd Table of contents:
Introduction
P1-0
Mathematical and Statistical Properties of Population Principal Components P10-20
Mathematical and Statistical Properties of Sample Principal Components Page 29-61
Principal Components as a Small Number of Interpretable Variables: Some Examples Page 63-77
Graphical Representation of Data Using Principal Components
Page 70-110
Choosing a Subset of Principal Components or Variables Page 111-149
Principal Component Analysis and Factor Analysis Page 150-166
Principal Components in Regression Analysis
Page 167-100
Principal Components Used with Other Multivariate Techniques Pages 199-231
Outlier Detection, Influential Observations, Stability, Sensitivity, and Robust Estimation of Principal Components
Page 233-364
Rotation and Interpretation of Principal Components
Page 360-208
Principal Component Analysis for Time Series and Other Non-Independent Data Pages 299-337
Principal Component Analysis for Special Types of Data
Page 336-373
Generalizations and Adaptations of Principal Component Analysis
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