Short description This comprehensive resource provides the algorithmic methods and state-of-the-art tools to successfully visualize statistical data. The coverage offers insight into underlying processes of density estimation, emphasizing use of visualization tools rather than only the theoretical concepts of classification and regression. The book is highly interactive in nature, as all figures and experiments can be reproduced via the two R software packages used throughout and available on a related Web site. Over 200 illustrations depict the discussed visualizations and "Examples" sections, making this both an dynamic text for students and a working reference for professionals.
From the contents Preface.
Introduction.
PART I VISUALIZATION.
1. Visualization of Data.
2. Visualization of Functions.
3. Visualization of Trees.
4. Level Set Trees.
5. Shape Trees.
6. Tail Trees.
7. Scales of Density Estimates.
8. Cluster Analysis.
PART II ANALYTICAL AND ALGORITHMIC TOOLS.
9. Density Estimation.
10. Density Classes.
11. Lower Bounds.
12. Empirical Processes.
13. Manipulation of Density Estimates.
PART III TOOLBOX OF DENSITY ESTIMATORS.
14. Local Averaging.
15. Minimization Eestimators.
16 Wavelet Estimators.
17. Multivariate Adaptive Hhistograms.
18. Best Basis Selection.
19. Stagewise Minimization.
Appendix A: Notations.
Appendix B: Formulas.
Appendix C: The parentchild relations in a modegraph.
