High Dimensional Statistics: A Non Asymptotic Viewpoint
Wainwright, Martin J.
High Dimensional Statistics: A Non Asymptotic Viewpoint - Cambridge Cambridge University Press 2019 - 552p
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 bound
9781108498029
Mathematical Statistics - Big Data
519.5 / WAI
High Dimensional Statistics: A Non Asymptotic Viewpoint - Cambridge Cambridge University Press 2019 - 552p
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 bound
9781108498029
Mathematical Statistics - Big Data
519.5 / WAI