| Lecture 1 |
Introduction |
| Lecture 2 |
Influence Functions- Heuristics |
| Lecture 3 |
Nonexistence of Higher Order Influence Functions |
| Lecture 4 |
Estimation of Quadratic Functionals- Upper Bounds |
| Lecture 5 |
The Haar Wavelet and Hölder Spaces |
| Lecture 6 |
Estimation of Quadratic Functionals- Lower Bounds |
| Lecture 7 |
Nonparametric Hypothesis Testing- The Minimax Separation Framework |
| Lecture 8 |
Goodness of Fit Tests in Density Models in L2 Separation |
| Lecture 9 |
Adaptive Estimation of Quadratic Functionals- Lepski's Method and Upper Bound |
| Lecture 10 |
Adaptive Estimation of Quadratic Functionals- Constrained Risk Inequality and Lower Bound |
| Lecture 11 |
Honest Adaptive Confidence Sets in L2- Generic Constructions |
| Lecture 12 |
Honest Adaptive Confidence Sets in L2 in Density Models- The Adaptation Region |
| Lecture 13 |
Multiresolution Analysis, Compactly Supported Wavelet Bases, Approximations in Besov Spaces. |
| Lecture 14 |
Testing Regularity in a Density Model |
| Lecture 15 |
Honest Adaptive Confidence Sets in L2 in Density Models- The Complete Picture |
| Lecture 16 |
The Theory of Influnce Functions- First and Higher Order |