Principal Component Analysis
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...
| Κύριος συγγραφέας: | |
|---|---|
| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
New York, NY :
Springer New York,
2002.
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| Έκδοση: | Second Edition. |
| Σειρά: | Springer Series in Statistics,
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 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.
