Location & approach

By car

The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B1 / A40. The best interstate exit to take from A45 is “Dortmund-Eichlinghofen” (closer to South Campus), and from B1 / A40 “Dortmund-Dorstfeld” (closer to North Campus). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B1-bridge leading into Dortmund.

For travelling to the Department of Statistics, convenient parking places can be found at Vogelpothsweg (Gates 21 / 24) or alternatively at the Otto-Hahn-Straße (Gates 28 / 30 / 35).

By train and bus

TU Dortmund University has its own train station (“Dortmund Universität”). From there, suburban trains (S-Bahn) leave for Dortmund main station (“Dortmund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.

You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop “Dortmund Kampstraße”. From there, take U43 or U44 to the stop “Dortmund Wittener Straße”. Switch to bus line 447 and get off at “Dortmund Universität S”.

The H-Bahn

The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on North Campus. One (“Dortmund Universität S”) is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Campus) Eichlinghofen. The other station is located at the dining hall at North Campus and offers a direct connection to South Campus every five minutes.

By plane

The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.

Plan of site & interactive map

The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent "Technologiepark".

> Go to interactive map

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Contrast Deutsch

Location & approach

By car

The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B1 / A40. The best interstate exit to take from A45 is “Dortmund-Eichlinghofen” (closer to South Campus), and from B1 / A40 “Dortmund-Dorstfeld” (closer to North Campus). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B1-bridge leading into Dortmund.

For travelling to the Department of Statistics, convenient parking places can be found at Vogelpothsweg (Gates 21 / 24) or alternatively at the Otto-Hahn-Straße (Gates 28 / 30 / 35).

By train and bus

TU Dortmund University has its own train station (“Dortmund Universität”). From there, suburban trains (S-Bahn) leave for Dortmund main station (“Dortmund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.

You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop “Dortmund Kampstraße”. From there, take U43 or U44 to the stop “Dortmund Wittener Straße”. Switch to bus line 447 and get off at “Dortmund Universität S”.

The H-Bahn

The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on North Campus. One (“Dortmund Universität S”) is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Campus) Eichlinghofen. The other station is located at the dining hall at North Campus and offers a direct connection to South Campus every five minutes.

By plane

The AirportExpress is a fast and convenient means of transport from Dortmund Airport (DTM) to Dortmund Central Station, taking you there in little more than 20 minutes. From Dortmund Central Station, you can continue to the university campus by interurban railway (S-Bahn). A larger range of international flight connections is offered at Düsseldorf Airport (DUS), which is about 60 kilometres away and can be directly reached by S-Bahn from the university station.

Plan of site & interactive map

The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent "Technologiepark".

> Go to interactive map

ServicePortal UniMail

Theses

This page lists the current and finished theses at the Chair of Business and Social Statistics.

Current

Currently running theses

Darwin Ngoune	Nichtparametrische Schätzung von Kovarianzmatrizen aus räumlichen Daten	Master
Michael Schweitzer	On Robustifications of Portmanteau [together with Prof. Dr. Roland Fried]	Master
Las Grönberg	Stock recommendation using graph neural networks and economy news data	Master
Yat Chun Fung	Multimodal Time-LLM: Integrating visual and temporal data for time series forecasting using large language models	Master
Vipul Chauhan	Comparative analysis of products perception using unsupervised machine learning and LLMs	Master
Lukas Dienst	Short-term electricity price forecasting of continuous intraday trading using distributional neural networks	Master
Nishat Tasnim Ahmed Meem	Parameter optimization for RollingLDA	Master
Kjell Noack	Können Zeitungsartikel in ökonometrischen Vorhersagen helfen? Eine VAR-basierte Prognose wirtschaftlicher Kennzahlen mithilfe der IPI- und UPI-Indizes	Bachelor
Larissa Sander	Prüfung und Anwendung des Small-Area-Verfahrens zur Bestimmung von regionalisierten Armutsmaßen in der amtlichen Statistik am Beispiel NRWs. (Betreuung TU Dortmund: Josef Schäfer, Betreuung IT.NRW: Dr. Johannes Rohde)	Master

Finished

Finished Theses at TU Dortmund University

Rohan Kumar Nayak	Exploration of unsupervised language-style transfer (ULST) methods	Master	2024
Darya Lukashina	Modellierung der Zielgruppe und Beliebtheit in Deutschland veröffentlichter Manga — Vergleich von Modellen auch mithilfe von Textdatenanalyse	Bachelor	2024
Mariia Hrechyn	Data preparation & training of LLMs in e-commerce settings	Master	2024
Niklas Herzog	Die Zukunft des Sportjournalismus: Einsatz von Sprachmodellen zur Erstellung von Spielberichten	Master	2024
Veronika Tsishetska	ChatGPT as a negotiator: Analyzing its adherence to principles of proportionality and equality	Master	2024
Muhammad Mahir Hasan Chowdhury	Attaching PET (-like) models to RELATIO to find causal relationships between narratives	Master	2024
Miguel Tedjouka Kankeu	Joint autoregressive modeling of real-valued and integer-valued time series data	Master	2024
Marlies Hafer	Weighted bootstrap consistency for matching estimators: detecting misspecification of the bias-correction	Master	2024
Aymane Hachcham	Diachronic Sense Modelling with Hierarchical Word Embeddings	Master	2024
Imene Kolli	Semantic shift modelling using graph neural networks	Master	2024
Lars Wilmes	Graphon Estimation	Master	2023
Jorge Alejandro Colin Paez	Comparing VAR-Based Forecasting Approaches in the Presence of Exogenous Shocks	Bachelor	2023
Christopher Gerlach	Lasso and Fusion Penalization for periodic SVARs	Master	2023
Priyanka Madiraju	Comparison of Diachronic Embeddings with Pre-trained Model Embeddings for Historical Texts	Master	2023
Jannik Bloß	Comparison of Active Learning techniques for the benefit of data set generation in the field of text mining	Bachelor	2023
Daniel Schürmann	An analysis of municipal vehicle data using classification methods and kernel density estimation [together with Diana Andrä, City of Dortmund, Dortmunder Statistik]	Master	2023
Fabian Blunck	Prediction Intervals for Generalized Random Forests [together with Prof. Dr. Christoph Hanck, UDE, Award for outstanding thesis (Master Degree), Alumni-Verein Dortmunder Statistikerinnen und Statistiker]	Master	2022
Cabrel Teguenme	AR-Sieve Bootstrap for the Random Forest and a simulation-based comparison with rangerts time series prediction [together with Prof. Dr. Markus Pauly]	Master	2022
Aylin Girona	Bootstrapping für Propensity Score Matching	Bachelor	2022
Yulia Shrub	Test data-based nowcasting of German GDP growth using newspaper data	Master	2022
Dmitri Artjuch	Prediction in Modern Distribution Grids with Renewable Energy	Master	2022
Kai-Robin Lange	Resampling strategies for unsupervised sentiment analysis using lexicon-based text embedding methods	Master	2021
Daniel Dzikowski	Periodic structural VAR analysis	Master	2021
Sven Ziegler	Prediction intervals for time series – a comparison of model-based and statistical learning techniques [together with Prof. Dr. Markus Pauly]	Master	2021
Erik Weber	Parametrisierung einer Versicherungssparte für ein internes Risikomodell am Beispiel der kraftfahrt-Haftpflicht-Sparte des Continentale-Versicherungverbunds	Bachelor	2021
Taha Abdolkarimi	Vorhersage von Aktienmärkten - ein Vergleich von Zeitreihen- und KünstlicheIntelligenz (KI)-Methoden [together with Prof. Dr. Markus Pauly]	Master	2021
Axel Preis	Untersuchung von Bootstrap-Verfahren für Quantilsautoregressionen	Master	2020
Maxime Faymonville	Bootstrap-based prediction for INAR processes	Master	2020
Carolin Wäscher	Untersuchung schwacher Instrumente bei linearer Regression mit Instrumentalvariablen	Bachelor	2020
Marlies Hafer	Vergleich von Bias-korrigierten Matching-Schätzern für den Average Treatment Effect	Bachelor	2020
Raphael Meixner	Comparing penalisation approaches for high-dimensional ARCH processes	Master	2020
Philipp Stockhaus	Vergleich der Zuverlässigkeit von Verfahren zur künstlichen Kontrollgruppenbildung mittels Krankenkassendaten	Master	2020
Guy Merlin Tchamegni	Budget Stress Test, credit Risk Roll Rate Modelling and Projection	Master	2019
Barbara Brune	On Subgraph Counts and Goodness-Of-Fit Testing for Stochastic Network Models [Preis für herausragende Abschlussarbeit (Abschlussart Master), Alumni-Verein Dortmunder Statistikerinnen und Statistiker]	Master	2019
An Viet Nguyen	Modellierung und Prognose der Bundestagswahlen mit Hilfe von VAR-Modellen kompositioneller Daten	Bachelor	2019
Fabian Erdmann	Mixed-Frequency Analyse makroökonomischer Daten mittels MIDAS	Bachelor	2019
Daniel Dzikowski	Volatilitätsanalyse von Log-Renditen mittels multivariater GARCH	Bachelor	2019
Johannes-Markus Yar	Bewertung von Modellrisiken durch Challengermodelle und –analysen - Conditional Inference Tree und Random Forest als Alternative zum klassischen Ratingverfahren	Master	2018
Philipp Hallmeier	Spektraldichteschätzung mittels bootstrapbasiertem Thresholding der Fourierkoeffizienten	Master	2018

Mannheim (until 02/2018)

Finished Theses at the University of Mannheim

Tobias Krabel	Residual Value Forecasting Using Tree-based Ensemble Methods: an Application to the Automobile Industry	Master	2018
Shaikh Tanvir Hossain	Lasso & Vector Autoregressive Models - with Applications to Dynamic Stochastic Networks	Master	2017
Marius Barckmann	On Germany's Intraday Power Market: Forecasting and Price Path Simulation	Master	2017
Felix Prenzel	A comparison of different asymmetric GARCH models for financial data	Bachelor	2017
Richard Grandpierre	Interest Revenue Forecasts for German Banks. A Dynamic Regression Tree Approach	Master	2016
Julia Steinmetz	On the Network Topology of Variance Decompositions: Measuring the Connectedness of Financial Firms	Bachelor	2016
Lukas Müller	Backtesting and Risk Measures	Bachelor	2016
Florian Böser	Discrete Time Series Models and their Applications to Networks	Master	2015
Florian Böser	Asymptotics of empirical autocovariances and autocorrelations in weakly linear models	Bachelor	2013