General information
Examination dates Prof. Redenbach:
February 19th, 2024 (with the morning being blocked for joint exams of measure theory/ODE)
March 14th, 2024
April 5th, 2024
April 18th, 2024
Examination dates Dr. Stockis:
February 15th, 2024
March 21st, 2024
April 18th, 2024
Please register yourself in person at the Dean's Office with Heike Sternike (Building 48, 511). Please bring your student ID with you.
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.
Mathematical Statistics
Contents
- 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.
Image Analysis for stochastic structures
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
Seminar Topological Data Analysis
Contents
The seminar will start with an introduction to topological data analysis (TDA). No prior knowledge on TDA is required. Then, we will focus on statistical methods based on TDA in various fields of application.
Registration via URM.
The first meeting for setting up the schedule will take place in the second week of the lecture period.
The Reading Course serves as preparation for the Master's thesis. The assignment of topics takes place individually. Please contact Prof. Redenbach if you would like to take a Reading Course in Statistics.