Principal component analysis / I.T. 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 applica...
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Full Text (via Springer) |
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| Format: | eBook |
| Language: | English |
| Published: |
New York, NY :
Springer,
[2002]
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| Edition: | Second edition. |
| Series: | Springer series in statistics.
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| Subjects: |
Table of Contents:
- 1. Introduction
- 2. Properties of Population Principal Components
- 3. Properties of Sample Principal Components
- 4. Interpreting Principal Components: Examples
- 5. Graphical Representation of Data Using Principal Components
- 6. Choosing a Subset of Principal Components or Variables
- 7. Principal Components Analysis and Factor Analysis
- 8. Principal Components in Regression Analysis
- 9. Principal Components Used with Other Multivariate Techniques
- 10. Outlier Detection, Influential Observations and Robust Estimation
- 11. Rotation and Interpretation of Principal Components
- 12. PCA for Time Series and Other Non-Independent Data
- 13. Principal Component Analysis for Special Types of Data
- 14. Generalizations and Adaptations of Principal Component Ananlysis
- Appendix A. Computation of Principal Components.