Universität Wien

040721 UK Selected Topics in Statistics (2021W)

3.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
ON-SITE
Moodle

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 07.10. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 14.10. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 21.10. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 28.10. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 04.11. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 11.11. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 18.11. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 25.11. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 02.12. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 09.12. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 16.12. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 13.01. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 20.01. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Thursday 27.01. 15:00 - 16:30 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course

Aims:
Get acquainted with concepts of Bayesian statistics: theoretical foundations, methodology and applications.
Learn how to implement computer based procedures.

Contents:
1. Decision Theory (admissibility and optimality, Bayes and minimax decisions)
2. Bayesian Estimation (Bayes formula, Bayes estimators, hierarchical and empirical Bayes methods)
3. Markov Chain Monte Carlo Methods (Slice Sampler, Gibbs Sampler, Metropolis Hastings, monitoring convergence, credible intervals)

Methods:
Lecture with exercise sessions, taking place on-site. Attendance of the first lecture and exercise sessions is compulsory.
Lecture notes, exercise sheets and data will be available online.
Students are supposed to code in statistical software.

Assessment and permitted materials

There is an oral exam and one exercise sheet (provided on 21.10.2021) on theoretical topics 1 and 2, as well as three exercise sheets with programming part on topic 3 (provided on 18.11.2021, 02.12.2021, 13.01.2022). The exercise sheets should be submitted in two weeks.

Minimum requirements and assessment criteria

40 points oral exam on lectures to topics 1 and 2
10 points for the exercise sheet to topics 1 and 2
15, 15 and 20 points for the exercise sheets on topic 3

The grade results according to the scheme: 4 from 50 points, 3 from 63 points, 2 from 75 points, 1 from 87 points.

Examination topics

All topics covered in the lecture.

Reading list

Shao, J. (2003): Mathematical Statistics.
Robert, C. P. And Casella, G. (2004): Monte Carlo Statistical Methods.
Hoff, P. D. (2010): A First Course in Bayesian Statistical Methods.

Association in the course directory

Last modified: Mo 18.10.2021 13:27