Lehre am Institut für Statistik

Kompetenzbereich Statistik

• Tutorium zu Schließende Statistik (270031)

  	Termine:	Lehrpersonen:
  	Mo. 09:15 - 10:45 | I-332 (Gruppe 1)	Tutor
  	Mo. 09:15 - 10:45 | I-442 (Gruppe 2)	Tutor
  	Mo. 09:15 - 10:45 | VII-004 (Gruppe 3)	Tutor
  	Mo. 14:30 - 16:00 | I-442 (Gruppe 4)	Tutor
  	Mo. 16:15 - 17:45 | I-442 (Gruppe 5)	Tutor
  	Di. 11:00 - 12:30 | I-342 (Gruppe 6)	Tutor
  	Di. 12:45 - 14:15 | I-342 (Gruppe 7)	Tutor
  	Di. 12:45 - 14:15 | III-115 (Gruppe 8)	Tutor
  	Mi. 09:15 - 10:45 | I-332 (Gruppe 9)	Tutor
  	Mi. 11:00 - 12:30 | I-342 (Gruppe 10)	Tutor
  	Mi. 11:00 - 12:30 | I-063 (Gruppe 11)	Tutor
  	Mi. 12:45 - 14:15 | I-342 (Gruppe 12)	Tutor
  	Mi. 12:45 - 14:15 | I-442 (Gruppe 13)	Tutor
  	Mi. 12:45 - 14:15 | I-063 (Gruppe 14)	Tutor
  	Mi. 14:30 - 16:00 | II-013 (Gruppe 15)	Tutor
  	Do. 09:15 - 10:45 | I-063 (Gruppe 16)	Tutor
  	Do. 11:00 - 12:30 | VII-005 (Gruppe 17)	Tutor
  	Do. 11:00 - 12:30 | I-063 (Gruppe 18)	Tutor
  	Do. 14:30 - 16:00 | I-342 (Gruppe 19)	Tutor
  	Do. 16:15 - 17:45 | I-063 (Gruppe 20)	Tutor
  	Fr. 09:15 - 10:45 | I-332 (Gruppe 21)	Tutor
  	Fr. 14:30 - 16:00 | I-442 (Gruppe 22)	Tutor
  	Fr. 14:30 - 16:00 | I-063 (Gruppe 23)	Tutor
  	Mo. 16:15 - 17:45 | VII-004 (Gruppe 24)	Tutor
  Bemerkungen:	Bereitschaft zur aktiven Mitarbeit in den eingeteilten Tutoriumsgruppen wird erwartet. Die Gruppeneinteilung beginnt über Stud.IP, ab dem Di, 07.04.2026 (1. Vorlesung) um 12:30:00 Uhr und endet am Di, 14.04.2026 um 23:59:00 Uhr.

• Schließende Statistik (270158)

  	Termine:	Lehrpersonen:
  	Di. 09:15 - 10:45 | VII-201	Sibbertsen
  Inhalt:	Normalverteilung Binomialverteilung Stichproben Punktschätzung Intervallschätzung Statistische Tests Regressionsanalyse
  Literatur:	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.

• Übung zu Schließende Statistik (270159)

  	Termine:	Lehrpersonen:
  	Di. 07:30 - 09:00 | VII-201	Sibbertsen

Kompetenzbereich (Area) Data Science and Applied Econometrics

• Statistical Programming (373005)

  	Termine:	Lehrpersonen:
  	Do. 16:15 - 17:45 | II-214	Rogge
  Inhalt:	Data Structures Functions and Loops Handling Data Graphics Linear Regression Numerical Optimization Monte Carlo Methods
  Literatur:	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)

  	Termine:	Lehrpersonen:
  	Di. 14:30 - 16:00 | I-063	Less
  Inhalt:	Kernel density estimation Nonparametric regression Semiparametric methods Machine learning
  Literatur:	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)

  	Termine:	Lehrpersonen:
  	Mi. 14:30 - 16:00 | I-063	Yu
  Inhalt:	Markov chain Metropolis Algorithm Adaptive Metropolis Algorithm Delayed Rejection Adaptive Metropolis Metropolis-Hastings Algorithm Monte Carlo Bootstrapping Markov Chain Monte Carlo
  Literatur:	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.
  Bemerkungen:	The course is divided into two main sections: lectures and R exercises.

• Machine Learning (373024)

  	Termine:	Lehrpersonen:
  	Mi. 09:15 - 10:45 | VII-004	Toumping Fotso
  Inhalt:	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.
  Literatur:	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.

Mehrere Kompetenzbereiche (Areas)

• Time Series Analysis (379016)

  	Termine:	Lehrpersonen:
  	Di. 11:00 - 12:30 | I-063	Sibbertsen
  Inhalt:	Stationarity Autoregressive und Moving Average Models Non-Stationarity Forecasting Spectral Analysis Long Memory Time Series.
  Literatur:	Hamilton, J. D. (1994): Time Series Analysis, Princeton. Schlittgen, R., Stritberg, H. J. (2003): Zeitreihenanalyse, Oldenbourg.

• Research Seminar Financial Markets and the Global Challenges (77782)

  	Termine:	Lehrpersonen:
  	Mi. 11:00 - 12:30 | I-442	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Reichert, Schneider, Schröder, Sibbertsen, Sönksen, Todtenhaupt
  Inhalt:	External guests present their latest research