General information
Examination dates Prof. Dr. Redenbach:
4th March, 2026
26th March, 2026
15th April, 2026
Examination dates Dr. Stockis:
10th February, 2026
4th March, 2026
31st March, 2026
Registration for the examination is to be conducted in person with Ms Huixia Lin-Jablonski (Building 48, 535). It is imperative that students present their student ID cards when they register.
Below are the lectures offered by our group in the winter semester.
If you would like to write a Bachelor's or Master's thesis in Statistics, please contact Prof. Redenbach.
Lectures in the summer term 2026
Contents:
- Linear regression models
- Parametric curve fitting
- Likelihood ratio tests
- Data adaptive model selection
- Analysis of Variance (ANOVA)
- Experimental design
- Stationary stochastic processes
- Autoregressive and ARMA-processes
- Parameter estimation and model selection for time series
- Trend and seasonality
- Forecasting by exponential smoothing and the Box-Jenkins method
- Linear filters
Contact time:
4 SWS / 50 h Lectures
2 SWS / 54 h Tutorials
Prerequisites (Contents):
Elementary probability theory and statistics (e.g. Praktische Mathematik: Stochastik)
Frequency of occurence:
Die Vorlesung wird regelmäßig im Sommersemester angeboten.
Contents:
Processing and statistical analysis of three-dimensional image data, in particula
- Random closed sets and their characteristics
- Discretisation and three-dimensional connectivity
- Mathematical morphology</il>
- Methods of image processing: filtering, segmentation, Euclidean distance transform, labelling, watershed transform
- Estimates of geometric characteristics for random closed sets from image data
Contact Time:
2 SWS / 26 h Vorlesungen
2 SWS / 26 h Übung/Praktikum
Prerequisites (Contents):
The lecture "Stochastic Methods" from the Bachelor degree program in mathematics.
Lectures in the winter term 2025/26
Content:
- Asymptotic analysis of M-estimators, especially of Maximum-Likelihood-estimators
- Bayes and Minimax-estimators
- Likelihood-ratio-tests: asymptotic analysis and examples (t-test, chi²-goodness-of-fit-test)
- Glivenko-Cantelli-theorem, Kolmogorov-Smirnov-test
- Differentiable statistic functionals and examples of applications (derivation of asymptotic results, robustness)
- Resampling methods on the basis of Bootstrap.
Contact time:
4 SWS / 60 h Lectures
2 SWS / 30 h Tutorials
Requirements:
Elementary probability theory and statistics (e.g. Praktische Mathematik: Stochastik)
Frequency of occurrence:
The lecture is given once per year, in the winter term.