Lecturer
Dr. Orkun Furat

Teaching assistant
Ly Viet Hoang

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Time and Place

Lecture
Monday, 16 - 18  (N24, H12)

Excercise session
Tuesday, 16 - 18  (N24, H12)   (every second week)

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Type

2 hours lecture and 1 hours excercise
Credit points: 4

Prerequisites

Probability Calculus ("Elementare Wahrscheinlichkeitsrechnung und Statistik") and Mathematical Statistics ("Stochastik I").

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Intended Audience

Bachelor and Master students in "Mathematik", "Wirtschaftsmathematik" and "Mathematische Biometrie"; students in "Master of Finance" and "Mathematical Data Science".

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Contents

In this lecture, students are particularly familiarized with computer-intensive methods of statistics and machine learning.  Main topics of the lecture are:

• multivariate normal distribution
• linear regression models
• tests for distributional assumptions
• non-linear regression models, e.g., neural networks

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Requirements and Exam

In order to participate in the final exam, it is necessary to earn 50% of the points on all problem sheets. Students who want to do so are kindly asked to register for the 'Vorleistung' in the LSF-'Hochschulportal'.

 

Literature

• Büning, H., Trenkler, G. (1994)
  Nichtparametrische statistische Methoden
  de Gruyter, Berlin

• Cressie, N.A. (1993)
  Statistics for Spatial Data
  J. Wiley & Sons, New York

• Dobson, A.J. (2002)
  An Introduction to Generalized Linear Models
  Chapmen & Hall, Boca Raton

• Hastie, T., Tibshirani, R., Friedman, J. (2001)
  The Elements of Statistical Learning
  Springer, New York

• Koch, K.R. (1997)
  Parameterschätzung und Hypothesentests in linearen Modellen
  Dümmlers-Verlag, Bonn

• Kroese, D.P., Chan, J.C.C. (2014)
  Statistical Modeling and Computation
  Springer, New York

• Lehmann, E.L. (1999)
  Elements of Large-Sample Theory
  Springer, New York

• Lehmann, E.L., Romano, E.J. (2005)
  Testing Statistical Hypotheses
  Springer-Verlag, New York

• Vapnik, V.N. (1998)
  Statistical Learning Theory
  J. Wiley & Sons, New York