Inference Theory, PhD course

Course content

Detailed schedule of lectures

Lecture Time Place Content Material
1 Mon 16/3, 13-15, MH227 Overview of the course content. Introduction to inference theory. Weak convergence on metric spaces. Ch (1), 18.1. Complementary reading on Measure theory and Topology
2 Thu 19/3, 13-15, MH227 Weak convergence on metric spaces. Ch 18.2-18.3.
3 Mon 23/3, 13-15, MH227 Empirical process theory for independent data. Ch 19.1-19.2.
4 Thu 26/3, 10-12, MH329 Empirical process theory for independent data. Ch 19.2, parts of Ch 19.3-19.4.
5 Functional differentiation for statistical functionals. Ch 20.1-20.2
6 Functional differentiation for statistical functionals. Applications to survival/duration analysis problems. Quantile estimation. Ch 20.3, 21.1-21.2, 21.4.
7 Quantile estimation. The bootstrap. Ch 21, 23
8 The bootstrap; empirical process results and Hamard differentiability. Ch 23
9 Thu 30/4, 13-15, MH227 Density estimation; estimation under smoothness assumptions. Optimal rates and limit distributions. Ch 24.1-24.3.
10 Mon 4/5, 13-15, MH227 Density estimation; estimation under order restrictions. Optimal rates and limit distributions. Ch 24.4. Complementary reading in (to be uploaded)
11 Thu 7/5, 13-15, MH227 The partial sum process. Applications to nonparametric regression (kernel smoothers). Van der Vaart and Wellner Ch xx. Handout material on Partial sum processes and regression. (to be uploaded)
12 Mon 11/5, 13-15 The partial sum process. Applications to nonparametric regression (kernel smoothers). Van der Vaart and Wellner Ch xx. Handout material on Partial sum processes and regression. (to be uploaded)
13 Thu 14/5, 13-15 Weak and long range dependence. Nonparametric inference for stationary processes. Papers by Taqqu, Taqqu and Dehling. Handout material on Partial sum processes and regression. (to be uploaded)
14 Mon 18/5, 13-15 Weak and long range dependence. Regression and kernel density estimation. Papers by Mielnichuk, and by Csorgo and Mielnichuk. Handout material on Partial sum processes and regression. (to be uploaded)
15 Thu 21/5, 13-15 The spectral measure and empirical spectral process for stationary processes. Spectral analysis. Papers by Dahlhaus, and Dahlhaus and Polonik.
16 Mon 25/5, 13-15 Spectral analysis. The periodogram; estimation of a smooth spectral density; estimation of a monotone spectral density. Papers by Dahlhaus, and by Anevski and Soulier.
17 Thu 28/5, 13-15 Parametric inference, M and Z-estimators. Asymptotic results for independent data Ch 5.1-5.3
18 tba M and Z-estimators. The ML estimator. Inference for dependent data Ch 5.4. Handout material on M and Z-estimation for dependent data.(to be uploaded)

Last modified: April 30 06.18:00 CET 2009 by Dragi Anevski