Abgeschlossene Projekte
Statistical Models based on Expectiles
The project aims at the modelling of distributional properties using non-parametric estimates. Classical non-parametric (mean-) regression will be extended to expectile regression. This allows to model the entire conditional distribution of a response variable instead of the mean value only. Numerous results from penalized spline estimation will be adapted to expectile regression which allows to derive asymptotic properties for the estimates. A particular modelling exercise is laid upon expectile estimation for longitudinal, clustered data. The dependence structure in the data is captured by incorporating individual components in the model, extending (linear) mixed models to mixed expectile models. Expectiles will also be compared and contrasted to quantiles serving as established benchmark model. All results of the project will be made available numerically with R packages.
Coordinator(s): Prof. Dr. Göran Kauermann
Staff: Dr. Linda Schulze Waltrup
Automatic classification and counting of fish species using sonar imaging tools
The increasing use of renewable energy sources leads - amongst others - to the construction of numerous new hydro-electric power plants. These power plants complicate the migration of fishes. Solutions like fish passes can help the fishes to migrate upstream but especially downstream this solution is not satisfactory. The goal of an interdisciplinary project involving biologists, engineers, computer scientists and statisticians is to find strategies that support the migration of fish. As part of this project we are developing statistical methods and - in cooperation with a computer scientist - a user-friendly software to classify and count fishes automatically using sonar videos.
Coordinator(s): Prof. Dr. Göran Kauermann