Mathematical Statistics

In mathematical statistics the aim is to analyze data sets (samples) to gain insight on a broader entirety. Students of this course will be introduced to the basics behind the theory of mathematical statistics in a comprehensive introduction. Essential estimation and test methods are presented. These methods will also be implemented in modern software languages accompanied by the application examples which will give students a deeper understanding of the theory. Furthermore, the goal is to teach all fundamentals needed for more advanced statistical purposes such as in biostatistics, actuarial sciences and finance.


Prof. Dr. Evgeny Spodarev

Viet Hoang

Time and Place

This course is organized entirely on Moodle.

Due to the current pandemic this course is organized entirely online. The following dates for the lectures and exercise sessions can be ignored for now. More information on Moodle.

Tuesday, 8-10, N24-H12
Thursday, 10-12, N24-H12

Exercise sessions
Wednesday, 16-18, N24-H14


4 hours lecture + 2 hours exercise session (ECTS 9)


  • Elementare Wahrscheinlichkeitsrechnung und Statistik
  • Wahrscheinlichkeitstheorie und Stochastische Prozesse

Target Audience

  • BSc Mathematik: Wahlpflicht Angewandte Mathematik
  • BSc Wirtschaftsmathematik: Wahlpflicht Stochastik/Optimierung/Finanzmathematik
  • BSc Mathematische Biometrie: Wahlpflicht Stochastik
  • MSc Mathematik: Wahlpflicht Angewandte Mathematik
  • MSc Wirtschaftsmathematik: Wahlplficht Stochastik/Optimierung/Finanzmathematik
  • MSc Mathematische Biometrie: Wahlpflicht Mathematik und Statistik
  • MSc Finance: Wahlpflicht Mathematik



  • Parametric models and fundamental theory
  • Exponential families, completeness, sufficiency
  • Point estimation
  • Properties of estimators (MSE, bias, consistency)
  • Best unbiased estimators, Cramer-Rao inequality
  • U-statistics, confidence intervals
  • Hypothesis testing
  • Density estimation or linear models (introduction)

Lecture Notes

See Moodle.

Exercise Sheets

See Moodle.


To participate in the final exam at least 50% of the homework points need to be achieved. Further information on Moodle.

Registration for the exam needs to be completed four days in advance of the exam date (only possible if the prerequisite for the exam is passed).


  • P. Bickel, K. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics, Prentice Hall
  • G. Casella, R.L. Berger, Statistical Inference, Duxbury
  • Lehmann, E. L., Casella, G. (2006). Theory of point estimation. Springer.
  • Lehmann, E. L., Romano, J. P. (2005). Testing statistical hypotheses. Springer.
  • Rüschendorf, L (2014). Mathematische Statistik. Springer.

Other useful literature to this course can be found in the Semesterapparat.




Prof. Dr. Evgeny Spodarev

Office hours: by appointment



Viet Hoang 

Office hours: by appointment


This course is organized entirely on Moodle.