Teaching at the Institute of Statistics

Area of Expertise in Statistics

• Tutorial on statistical inference (270031)

  	Time and room:	Lecturer:
  	Mon. 09:15 - 10:45 | I-332 (Group 1)	Tutor
  	Mon. 09:15 - 10:45 | I-442 (Group 2)	Tutor
  	Mon. 09:15 - 10:45 | VII-004 (Group 3)	Tutor
  	Mon. 14:30 - 16:00 | I-442 (Group 4)	Tutor
  	Mon. 16:15 - 17:45 | I-442 (Group 5)	Tutor
  	Tue. 11:00 - 12:30 | I-342 (Group 6)	Tutor
  	Tue. 12:45 - 14:15 | I-342 (Group 7)	Tutor
  	Tue. 12:45 - 14:15 | III-115 (Group 8)	Tutor
  	Wed. 09:15 - 10:45 | I-332 (Group 9)	Tutor
  	Wed. 11:00 - 12:30 | I-342 (Group 10)	Tutor
  	Wed. 11:00 - 12:30 | I-063 (Group 11)	Tutor
  	Wed. 12:45 - 14:15 | I-342 (Group 12)	Tutor
  	Wed. 12:45 - 14:15 | I-442 (Group 13)	Tutor
  	Wed. 12:45 - 14:15 | I-063 (Group 14)	Tutor
  	Wed. 14:30 - 16:00 | II-013 (Group 15)	Tutor
  	Thu. 09:15 - 10:45 | I-063 (Group 16)	Tutor
  	Thu. 11:00 - 12:30 | VII-005 (Group 17)	Tutor
  	Thu. 11:00 - 12:30 | I-063 (Group 18)	Tutor
  	Thu. 14:30 - 16:00 | I-342 (Group 19)	Tutor
  	Thu. 16:15 - 17:45 | I-063 (Group 20)	Tutor
  	Fri. 09:15 - 10:45 | I-332 (Group 21)	Tutor
  	Fri. 14:30 - 16:00 | I-442 (Group 22)	Tutor
  	Fri. 14:30 - 16:00 | I-063 (Group 23)	Tutor
  	Mon. 16:15 - 17:45 | VII-004 (Group 24)	Tutor
  Comments:	Active participation in the assigned tutorial groups is expected. Group registration will take place via Stud.IP, starting on Tuesday, 07 April 2026 (first lecture) at 12:30 p.m. and ending on Tuesday, 14 April 2026 at 11:59 p.m.

• Statistical Inference (270158)

  	Time and room:	Lecturer:
  	Tue. 09:15 - 10:45 | VII-201	Sibbertsen
  Contents:	Normal Distribution Binomial Distribution Sampling Point Estimation Intervall Estimation Statistical Testing Linear Regression
  Literature:	Sibbertsen, P./Lehne, H. (2014) Statistik, 2. Auflage, Berlin. Fahrmeir, L. et al. (2004) Statistik, 5. Auflage Berlin. Schlittgen, R. (2003) Einführung in die Statistik, 10. Auflage München.

• Statistical Inference (270159)

  	Time and room:	Lecturer:
  	Tue. 07:30 - 09:00 | VII-201	Sibbertsen

Area Data Science and Applied Econometrics

• Statistical Programming (373005)

  	Time and room:	Lecturer:
  	Thu. 16:15 - 17:45 | II-214	Rogge
  Contents:	Data Structures Functions and Loops Handling Data Graphics Linear Regression Numerical Optimization Monte Carlo Methods
  Literature:	Ligges (2007) Programmieren mit R, Berlin, Springer. Braun / Murdock (2007) A first course in statistical programming with R, Cambridge University Press. Rizzo (2008) Statistical Computing with R, Chapman & Hall.

• Nonparametric Statistical Methods (373010)

  	Time and room:	Lecturer:
  	Tue. 14:30 - 16:00 | I-063	Less
  Contents:	Kernel density estimation Nonparametric regression Semiparametric methods Machine learning
  Literature:	Härdle, W. (1992) Applied Nonparametric Regression, Cambridge University Press. Henderson, D. J., Parmeter, C. F. (2015) Applied Nonparametric Econometrics, Cambridge University Press. Li, Q., Racine, J. S. (2007) Nonparametric Econometrics, Princeton University Press. Pagan, A., Ullah A. (1999): Nonparametric Econometrics, Cambridge University Press. Friedman, J., Hastie, T., Tibshirani, R., (2001): The Elements of Statistical Learning, Springer.

• Computerintensive Statistics (373015)

  	Time and room:	Lecturer:
  	Wed. 14:30 - 16:00 | I-063	Yu
  Contents:	Markov chain Metropolis Algorithm Adaptive Metropolis Algorithm Delayed Rejection Adaptive Metropolis Metropolis-Hastings Algorithm Monte Carlo Bootstrapping Markov Chain Monte Carlo
  Literature:	Albert, Jim (2007), Bayesian Computation with R, Springer J.S Urban Hjorth(1994), Computer Intensive Statistical Methods, Physica Heidelberg, 1993. W.R.Gilks, S, Richardson and D.J. Spiegelhalter, Markov Chain Monte Carlo in Practice. Chapman & Hall / CRC, USA, 1996.
  Comments:	The course is divided into two main sections: lectures and R exercises.

• Machine Learning (373024)

  	Time and room:	Lecturer:
  	Wed. 09:15 - 10:45 | VII-004	Toumping Fotso
  Contents:	The term machine learning summarises a wide range of statistical methods used for pattern recognition, classification and prediction. Applications encompass the recognition of text, speech and images, spam and fraud detection, recommendation systems for customers, as well as generating information from large quantities of data or predicting stock prices. This lecture covers a selection of common supervised to unsupervised learning algorithms. Linear and logistic regression XGBoost, AdaBoost, Gradient Boosting Regression and classification trees Model selection and cross-validation Support vector machines Neural networks (maybe) The lecture includes applications in Python. Previous experience with Python or R is helpful, but not required.
  Literature:	Friedman, J., Hastie, T., & Tibshirani, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Vol. 2). Springer, Berlin: Springer Series in Statistics.

Several Areas

• Time Series Analysis (379016)

  	Time and room:	Lecturer:
  	Tue. 11:00 - 12:30 | I-063	Sibbertsen
  Contents:	Stationarity Autoregressive und Moving Average Models Non-Stationarity Forecasting Spectral Analysis Long Memory Time Series.
  Literature:	Hamilton, J. D. (1994): Time Series Analysis, Princeton. Schlittgen, R., Stritberg, H. J. (2003): Zeitreihenanalyse, Oldenbourg.

• Finance Research Seminar (77782)

  	Time and room:	Lecturer:
  	Wed. 11:00 - 12:30 | I-442	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Reichert, Schneider, Schröder, Sibbertsen, Sönksen, Todtenhaupt
  Contents:	External guests present their latest research