Information about Prof. Haupt and the staff members of the Chair of Statistics and Data Analytics as well as the contact details can be found on the team's web page.
The course offerings of the Chair of Statistics and Data Analytics include methods in statistics at the undergraduate, master's, and graduate levels. Emphasis is placed on linking knowledge of statistical methods with computational skills for applying and interpreting this knowledge.
Our research focuses on the development and application of flexible regression methods. Our work covers basic research as well as applied statistics. We are continuously working on interdisciplinary practical projects together with our scientific, business, and societal partners.
Several (related) topics are currently available for Master's or Bachelor's theses (and "Zulassungsarbeiten"):
Work can be applied, computational, theoretical. Connected topics are available. For further information, please contact Prof. Haupt.
Bauer I., Haupt H., and S. Linner [2024] Pinball boosting of regression quantiles. Computational Statistics and Data Analysis.
Fritsch M., Haupt H., J. Schnurbus [2024] Efficiency of poll-based multi-period forecasting systems for German state elections. International Journal of Forecasting.
Fritsch M., Pua A. A. Y. and J. Schnurbus [2024]
Teaching Advanced Topics in Econometrics using Introductory Textbooks: The Case of Dynamic Panel Data Methods
International Review of Economics Eduction, 47, 100297
Ranpal S., von Bargen S., Gilles S., Luschkova D., Landgraf M., Bogawski P., Traidl-Hoffmann C., Büttner C., Damialis A., Fritsch M., and S. Jochner-Oette [2024] Continental-scale Evaluation of Downy Birch Pollen Production: Estimating the Impacts of Global Change. Environmental Research, 252, 119114
Jetschni J., Fritsch M., and S. Jochner-Oette [2023]
How does pollen production of allergenic species differ between urban and rural environments?
International Journal of Biometeorology, 67, 1839-1852
Wild M., Behm S., Beck C., Cyris J., Schneider A., Wolf K., and H. Haupt [2022]
Mapping the time-varying spatial heterogeneity of temperature processes over the urban landscape.
Urban Climate, 101160.
Haupt H. and M. Fritsch [2022]
Quantile Trend Regression and Its Application to Central England Temperature.
Mathematics 2022, 10 (3), 413
Fritsch M. and S. Behm [2021]
Data for modeling nitrogen dioxide concentration levels across Germany.
Data in Brief, 38, 107324
Kleinke K., Fritsch M., Stemmler M., Reinecke J., and F. Lösel [2021]
Quantile Regression-Based Multiple Imputation of Missing Values - An Evaluation and Application to Corporal Punishment Data.
Methodology, 17 (3), 205-230
Fritsch M. and S. Behm [2021]
Agglomeration and infrastructure effects in land use regression models for air pollution - Specification, estimation, and interpretations.
Atmospheric Environment, 253, 118337
Fritsch M., Pua A. A. Y. and J. Schnurbus [2021]
pdynmc: A Package for Estimating Linear Dynamic Panel Data Models Based on Nonlinear Moment Conditions.
The R Journal, 13 (1), 218-231
Behm S. and H. Haupt [2020]
Predictability of hourly nitrogen dioxide concentrations,
Ecological Modelling, 428, 109076
Fritsch M., Pua A. A. Y. and J. Schnurbus [2020]
pdynmc: Moment Condition Based Estimation of Linear Dynamic Panel Data Models.
CRAN: https://cran.r-project.org/web/packages/pdynmc/ ; alternatively, see: https://github.com/markusfritsch/pdynmc
Fritsch M., Haupt H., Lösel F. and M. Stemmler [2019]
Regression trees and random forests as alternatives to classical Regression modeling: Investigating the risk factors for corporal punishment.
Psychological Test and Assessment Modelling 61 (4), 389-417
Behm S., Haupt H. and A. Schmid [2018]
Spatial detrending revisited: Modelling local trend patterns in NO2-concentration in Belgium and Germany.
Spatial Statistics 28, 331-351
Haupt H., Schnurbus J. and W. Semmler [2018]
Estimation of grouped, time-varying convergence in economic growth.
Econometrics and Statistics 8, 141-158
Scholz M., Schnurbus J., Haupt H., Dorner V., Landherr A. and F. Probst [2018]
Dynamic Effects of User- and Marketer-Generated Content on Consumer Purchase Behavior: Modeling the Hierarchical Structure of Social Media Websites.
Decision Support Systems 113, 43-55
