Table of Contents
1. Introduction;
2. Basic tail and concentration bounds;
3. Concentration of measure;
4. Uniform laws of large numbers;
5. Metric entropy and its uses;
6. Random matrices and covariance estimation;
7. Sparse linear models in high dimensions;
8. Principal component analysis in high dimensions;
9. Decomposability and restricted strong convexity;
10. Matrix estimation with rank constraints;
11. Graphical models for high-dimensional data;
12. Reproducing kernel Hilbert spaces;
13. Nonparametric least squares;
14. Localization and uniform laws;
15. Minimax lower bounds; References; Author index; Subject index.