Chair of Statistics and Data Analytics

For the planning and orientation of our students, the Chair of Statistics and Data Analytics and the Computational Statistics and Mathematics teaching unit offer a joint overview of the planned courses for the period winter semester 2022/23 to summer semester 2024.

The Chair of Statistics and Data Analytics is offering bachelor or master theses on statistical market research/decision support topics related to revenue forecasting using data from the EFRE-funded project DIGIONAL. For details on the topic and application modalities, please contact Prof. Haupt.

Several (related) topics are currently available for Master's or Bachelor's theses (and "Zulassungsarbeiten"):

• Time series forecasts (and their averages) over multiple horizons and information sets (prerequisite is a bachelor level knowledge of time series analysis/stochastic processes)
• Forecast evaluation (prerequisite is a bachelor level knowledge of time series analysis/stochastic processes)
• Deal curve models in marketing research (prerequisite is a basic knowledge in regression analysis and marketing research)
• Air quality monitoring and prediction  (prerequisite is a bachelor level knowledge of time series analysis/stochastic processes)
• Quantile regression and utility (prerequisite is a basic knowledge in regression analysis and expected utility)
• Regression smoothing  (prerequisite is a basic knowledge in regression analysis)
• Ridge functions in statistics  (prerequisite is a basic knowledge of analysis and mathematical statistics)
• Central limit theory for M-estimators (prerequisite is a basic knowledge of probability theory and mathematical statistics)

Work can be applied, computational, theoretical. Connected topics are available. For further information, please contact Prof. Haupt.

Fritsch M., Haupt H., and J. Schnurbus [2024], Efficiency of poll-based multi-period forecasting systems for German state elections. International Journal of Forecasting, forthcoming

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, forthcoming

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

Special Issue in "Mathematics" on "Statistical Modelling of Complex Environmental Time Series". Submission deadline: 30 September 2022

We are organizing a session on "Advances in quantile regression" (CO581) at the 16th CFE Conference, King's College London, 17-19 December 2022 and cordially invite you to contribute. 
The submission tool is now accessible from http://cfenetwork.org/CFE2022/submission.php until 21 September 2022.
If you have any questions contact us at harry.haupt@uni-passau.de and joachim.schnurbus@uni-passau.de