Team
Information about Prof. Haupt and the staff members of the Chair of Statistics and Data Analytics can be found on the team web page. Please also visit the team web page for contact details.
Teaching
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.
Research
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.
Latest news
Registration period: 15.04. to 29.04.2022
Viewing period: Monday, 09.05. to Thursday, 12.05.2022, how to:
For your planning, you get here the current update on the examination dates of the Chair of Statistics and Data Analytics and the Teaching Unit of Computational Statistics and Mathematics.
For all other centrally organized exams you can find the information directly on the homepage of the Examinations office.
| Titel | PNo. | Date | Format | Location | |
| Computergestützte Statistik - Einführung in R | BA | 212119 | Wed, 02.02.2022, | Häusliche Leistungsfeststellung (60 min.) | Stud.IP 35620 |
| Einführung in die Ökonometrie (Repeater) | BA | 212109 | Mon. 31.01.2022, | Oral Exam | Zoom |
| Einführung in die Zeitreihenanalyse | BA | 212107 | Thu, 10.02.2022, | Präsenzklausur (60 min.) | WiWi SR 027 |
| Computational Statistics - Regression in R | MA | 261170 | Tue, 08.02.2022, | Performance Assessment at home (60 min.) | Stud.IP 35621 |
| Computational Statistics - Statistical Learning in R | MA | 261001 | Tue, 01.02.2022, | Performance Assessment at home (60 min.) | Stud.IP 35622 |
| Econometric Methods | MA | 261120 | Mon, 07.02.2022, | Präsenzklausur (60 min.) | PHIL HS 1 |
| Fundamentals of Business Analytics | MA | 261003 | automatically via Ilias | ||
| Multivariate Verfahren | MA | 201504 | Mon, 14.02.2022, | Präsenzklausur (90 min.) | AM HS 9 |
| Mathematik für Wirtschaftswissenschaftler | BA | 210101 | Wed, 23.02.2022 | Präsenzklausur (120 min.); 1 or 2 cohorts | |
| Statistik für Wirtschaftswissenschaftler | BA | 250601 | Thu, 24.02.2022 | Präsenzklausur (120 min.) |
In WS 2021/22, the courses of the Chair of Statistics and Data Analytics and the Computational Statistics and Mathematics teaching unit will be held as follows:
Online
Mathematik für Wirtschaftswissenschaftler
Statistik 1 für Wirtschaftswissenschaftler
Computergestützte Statistik - Einführung in R
Computational Statistics - Regression in R
Computational Statistics - Statistical Learning in R
Multivariate Verfahren
Fundamentals of Business Analytics
On-site (on campus)
Einführung in die Zeitreihenanalyse
Econometric Methods
Details can be found in Stud.IP. Please also pay attention to the current announcements in the forums of our courses.
As an orientation for all students concerned, 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 2021/22 to summer semester 2023.
The Chair of Statistics and Data Analytics is offering a bachelor's or master's thesis on the topic of Revenue Forecasting as part of the EFRE-funded project DIGIONAL. For details on the topic and application modalities, please refer to the announcement.
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. If you are interested, please contact Prof. Haupt.
Latest research news
Selected Publications (2018-2022):
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
Master@IBM: Duales Study program:
https://careers.ibm.com/job/13183124/master-ibm-data-science-machine-learning-m-w-x-remote/
Associate@IBM: Trainee-Program for Data Scientists:
Kontakt: Dr. Angelika Schmid
Special Issue in "Mathematics" on "Statistical Modelling of Complex Environmental Time Series". Submission deadline: 30 Sepetmber 2021
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 8 September 2022.
If you have any questions contact us at harry.haupt@uni-passau.de and joachim.schnurbus@uni-passau.de
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