http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
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.
Mathematical and Statistical Properties of Population Principal Components -- Mathematical and Statistical Properties of Sample Principal Components -- Principal Components as a Small Number of Interpretable Variables: Some Examples -- Graphical Representation of Data Using Principal Components -- Choosing a Subset of Principal Components or Variables -- Principal Component Analysis and Factor Analysis -- Principal Components in Regression Analysis -- Principal Components Used with Other Multivariate Techniques -- Outlier Detection, Influential Observations, Stability, Sensitivity, and Robust Estimation of Principal Components -- Rotation and Interpretation of Principal Components -- Principal Component Analysis for Time Series and Other Non-Independent Data -- Principal Component Analysis for Special Types of Data -- Generalizations and Adaptations of Principal Component Analysis.