Herbstsemester (jährlich wiederkehrende Veranstaltung)
The students will learn about methods of smoothing and application of concepts to data. The aim is to build sufficient interest in the topic as well as the ability to implement the methods to various different datasets.
- Parametric estimation methods: selection of important results
o Maximum likelihood
o Least squares: regression & diagnostics
- Nonparametric curve estimation
o Density estimation, Kernel regression, Local polynomials, Bandwidth selection
o
Selection of special topics (as time permits, we will cover as many
topics as possible) such as change points, modes & monotonicity,
robustness, partial linear models, roughness penalty, local likelihoods,
etc.
- Applications: potential areas of applications will be
discussed such as, change assessment, trend and surface estimation,
probability and quantile curve estimation, and others.