Principal Component Analysis

  • I. T. Jolliffe
Part of the Springer Series in Statistics book series (SSS)

About this book

Introduction

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.

Keywords

Factor analysis Regression analysis principal component analysis statistics time series

Authors and affiliations

  • I. T. Jolliffe
    • 1
  1. 1.Department of Mathematical Sciences King’s CollegeUniversity of AberdeenAberdeenUK

Bibliographic information

  • DOI https://doi.org/10.1007/b98835
  • Copyright Information Springer-Verlag New York, Inc. 2002
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-0-387-95442-4
  • Online ISBN 978-0-387-22440-4
  • Series Print ISSN 0172-7397
  • About this book