Institute of Mathematics > Departments > Applied Statistics

Research group Applied Statistics

The research group "Applied Statistics" has been headed by Prof. Dr. Jan Gertheiss till December 2018.


On the one hand, the group is focusing on application-driven methodological research, particularly regularization and ensemble methods for categorical, functional and high-dimensional data. On the other hand, we are offering consulting services on statistics and data analytics to collaborators from, e.g., economics, engineering and life sciences.


Furthermore, the working group participates in the DFG Project Scientific Network on the topic "Statistical postprocessing of ensemble forecasts for various weather quantities", conducted by Dr. Annette Möller. This project is concerned with developing and implementing statistical models for probabilistic weather forecasting.


We are teaching basic statistics to students from applied mathematics, (industrial) economics and management, industrial engineering, and (business) informatics. In addition, we are offering advanced classes to students from various disciplines.


Recent Publications:

  • Reicherzer, T., S. Häffner, T. Shahzad, J. Gronbach, J. Mysliwietz, C. Hübener, U. Hasbargen, J. Gertheiss, A. Schulze, S. Saverio, R.E. Morty, A. Hilgendorff, and H. Ehrhardt (2018): Activation of the NFkB pathway alters the phenotype of MSCs in the tracheal aspirates of preterm infants with severe BPD. American Journal of Physiology–Lung Cellular and Molecular Physiology, in press.
  • Gertheiss, J., J. Goldsmith, and A.-M. Staicu (2017): A note on modeling sparse exponential-family functional response curves. Computational Statistics & Data Analysis, 105, 46–52.
  • Trautmann, J., L. Meier-Dinkel, J. Gertheiss, and D. Mörlein (2017): Noise and accustomization: a pilot study of trained assessors’ olfactory performance. PLOS ONE.
  • Baran, S. and A. Möller (2016): Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature. Meteorology and Atmospheric Physics, to appear.
  • Feilke, M., B. Bischl, V.J. Schmid, and J. Gertheiss (2016): Boosting in nonlinear regression models with an application to DCE-MRI data. Methods of Information in Medicine, 55, 31–41.
  • Hess, W., G. Tutz, and J. Gertheiss (2016): A flexible link function for discrete-time duration models. Jahrbücher für Nationalökonomie und Statistik (Journal of Economics and Statistics), 236, 455–482.
  • Meier-Dinkel, L., J. Gertheiss, W. Schnäckel, and D. Mörlein (2016): Consumers’ perception and acceptance of boiled and fermented sausages from strongly boar tainted meat. Meat Science, 118, 34–42.
  • Möller, A. and J. Groß (2016): Probabilistic temperature forecasting based on an ensemble AR modification. Quarterly Journal of the Royal Meteorological Society, 142, 1385–1394.
  • Möller, A., G. Tutz, and J. Gertheiss (2016): Random Forests for Functional Covariates. Journal of Chemometrics, 30, 715–725.
  • Mörlein, D., J. Trautmann, J. Gertheiss, L. Meier-Dinkel, J. Fischer, H.-J. Eynck, L. Heres, C. Looft, and E. Tholen (2016): Interaction of skatole and androstenone in the olfactory perception of boar taint. Journal of Agricultural and Food Chemistry, 64, 4556–4565.
  • Scheipl, F., J. Gertheiss, and S. Greven (2016): Generalized functional additive mixed models. Electronic Journal of Statistics, 10, 1455–1492.
  • Sweeney, E., C. Crainiceanu, and J. Gertheiss (2016): Testing differentially expressed genes in dose-response studies and with ordinal phenotypes. Statistical Applications in Genetics and Molecular Biology, 15, 213–235.
  • Trautmann, J., L. Meier-Dinkel, J. Gertheiss, and D. Mörlein (2016): Boar taint detection: A comparison of three sensory protocols. Meat Science, 111, 92–100.
  • Tutz, G. and J. Gertheiss (2016): Regularized regression for categorical data (with discussion and rejoinder). Statistical Modelling, 16, 161–260.

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