General information
Examination dates Prof. Dr. Redenbach:
12th August, 2026
24th September, 2026
16th October, 2026
Examination dates Dr. Stockis:
5th August, 2026
17th September, 2026
Registration for the examination is to be conducted in person with Ms Huixia Lin-Jablonski (Office 48-535). It is imperative that students present their student ID cards when they register.
Please note the following exception to the examination registration:
- 06th – 17th July, 2026, with Ms. Barbara Ermisch (Office: 48-616),
- 20th – 24th July, 2026 with Ms. Kirsten Höffler (Office: 48-629)
Below are the lectures offered by our group in the sommer 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:
The lecture is given once per year, in the summer term.
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. Further knowledge of stochastics (e.g. ‘Time Series Analysis’ or ‘Probability Theory’) is an advantage, but not essential.
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.