You will find the latest research news on publications, projects and conferences on our homepage. A detailed overview of the research activities of all scientists at the Chair of Statistics and Data Analytics can be found on their personal pages.
The research group for Statistics and Data Analytics and Computational Statistics and Mathematics currently focuses on the development and application of almost model-free methods of semi- and nonparametric regression, typical for Data Science, Data Analytics, and Statistical Machine Learning. The focus is on multivariate nonlinear regression, in particular kernel- and spline regression-based methods for estimation
of moments, quantiles, and distributions. In our basic research, we deal with questions of weighting, smoothing, and loss functions on the modeling side. On the inference side, we investigate and develop asymptotic and simulation-based methods, mainly in the context of dependent and heterogeneous data-generating processes. We regularly apply concepts from our basic research to problems of applied statistics, currently in the fields of internet economy, air pollution, psychology, regional economics, market research and real estate markets.
- Asymptotic theory for nonlinear regression using weighted L1 loss
- Multiple nonparametric time series regression modelling and forecasting
- Nonlinear moment conditions for dynamic fixed effect panel data models
- Modelling and forecasting of spatio-temporal patterns in air pollutants