Teaching at the Institute of Statistics

Kompetenzbereich Statistik

• 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

• Tutorium zu Schließende Statistik (270031)

  	Termine:	Lehrpersonen:
  	Mo. 09:15 - 10:45 | I-442 (Gruppe 1)	Tutor
  	Mo. 09:15 - 10:45 | I-332 (Gruppe 2)	Tutor
  	Mo. 09:15 - 10:45 | VII-004 (Gruppe 3)	Tutor
  	Mo. 14:30 - 16:00 | VII-002 (Gruppe 4)	Tutor
  	Mo. 16:15 - 17:45 | I-442 (Gruppe 5)	Tutor
  	Di. 11:00 - 12:30 | I-442 (Gruppe 6)	Tutor
  	Di. 12:45 - 14:15 | I-401 (Gruppe 7)	Tutor
  	Di. 12:45 - 14:15 | I-442 (Gruppe 8)	Tutor
  	Di. 12:45 - 14:15 | I-112 (Gruppe 9)	Tutor
  	Di. 14:30 - 16:00 | I-112 (Gruppe 10)	Tutor
  	Mi. 11:00 - 12:30 | I-063 (Gruppe 11)	Tutor
  	Mi. 12:45 - 14:15 | I-442 (Gruppe 12)	Tutor
  	Mi. 12:45 - 14:15 | I-112 (Gruppe 13)	Tutor
  	Mi. 14:30 - 16:00 | I-442 (Gruppe 14)	Tutor
  	Mi. 16:15 - 17:45 | VII-004 (Gruppe 15)	Tutor
  	Do. 09:15 - 10:45 | I-342 (Gruppe 16)	Tutor
  	Do. 11:00 - 12:30 | I-301 (Gruppe 17)	Tutor
  	Do. 11:00 - 12:30 | VII-005 (Gruppe 18)	Tutor
  	Do. 11:00 - 12:30 | I-063 (Gruppe 19)	Tutor
  	Do. 16:15 - 17:45 | I-063 (Gruppe 20)	Tutor
  	Fr. 09:15 - 10:45 | I-332 (Gruppe 21)	Tutor
  	Fr. 09:15 - 10:45 | I-063 (Gruppe 22)	Tutor
  	Fr. 11:00 - 12:30 | I-332 (Gruppe 23)	Tutor
  	Fr. 11:00 - 12:30 | I-063 (Gruppe 24)	Tutor
  Bemerkungen:	Bereitschaft zur aktiven Mitarbeit in den eingeteilten Tutoriumsgruppen wird erwartet. Die Gruppeneinteilung findet über Stud.IP vom 8.4.2025 16:30 bis 11.4.2025 23:59 statt.

Kompetenzbereich (Area) Empirical Economics and Econometrics

• Statistical Programming (373005)

  	Termine:	Lehrpersonen:
  	Mo. 14:30 - 16:00 | I-233	Kreye
  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:
  	Mi. 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.

• Multivariate Statistics (373011)

  	Termine:	Lehrpersonen:
  	Mo. 12:45 - 14:15 | I-063	Fitter
  Inhalt:	Short overview of Matrix and Vector Algebra Multivariate descriptive statistics Multivariate Distributions Multivariate analysis of variance Introduction to data analysis Cluster Analysis Principal Components Analysis and Factor Analysis.
  Literatur:	Izenman, A. J. (2008). Modern multivariate statistical techniques. Regression, classification and manifold learning. Johnson R. A. and Wichern D. W., (2007), Applied Multivariate Statistical Analysis. 6th edition. New Jersey: Pearson. Krzanowski, W. (2000). Principles of multivariate analysis (Vol. 23). OUP Oxford. Rencher, A. C. and Christensen W. F. (2012). Methods of multivariate analysis.3rd edition. John Wiley & Sons.

• Computerintensive Statistics (373015)

  	Termine:	Lehrpersonen:
  	Mi. 12:45 - 14:15 | I-063	Yu
  Inhalt:	Metropolis Algorithm Adaptive Metropolis Algorithm Delayed Rejection Adaptive Metropolis Metropolis-Hastings Algorithm Gibbs Sampling
  Literatur:	Albert, Jim (2007): Bayesian Computation with R, Springer J.S Urban Hjorth(1994) Computer Intensive Statistical Methods

• Machine Learning (373024)

  	Termine:	Lehrpersonen:
  	Mi. 09:15 - 10:45 | I-342	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. These refer to clustering and regression problems, and clustering and dimensionality reduction methods respectively. Examples of covered statistical methods include: Linear and logistic regression K-nearest neighbours Naïve Bayes Model selection and cross validation Tree-based methods Support vector machines Principal component analysis Neural networks 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. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R (Vol. 112). New York: Springer. Bishop, C. (2006): Pattern Recognition and Machine Learning, Springer, New York: Information Science and Statistics Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. Wasserman, L. (2013). All of Statistics: A Concise Course in Statistical Inference. Springer Science & Business Media.

Mehrere Kompetenzbereiche (Areas)

• Time Series Analysis (379016)

  	Termine:	Lehrpersonen:
  	Di. 14:30 - 16:00 | I-442	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.

• Lecture Series: Financial Markets and the Global Challenges (379059)

  	Termine:	Lehrpersonen:
  	Di. 16:15 - 17:45 | I-401	Blaufus, Dierkes, Dräger, Gassebner, Prokopczuk, Reichert, Schneider, Schröder, Sibbertsen, Sönksen, Todtenhaupt
  Inhalt:	Financial markets are the backbone of the economy. The world is facing many challenges such as climate change, crime and international conflicts, ageing societies or economic disruptions. In this lecture series, faculty members of the School of Economics and Management will discuss how financial markets are related and/or might provide means to tackle these challenges.

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

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