040721 UK Selected Topics in Statistics (MA) (2022W)

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).

Registration is open from Mo 12.09.2022 09:00 to Fr 23.09.2022 12:00
Deregistration possible until Sa 15.10.2022 23:59

Details

max. 30 participants

Language: English

Lecturers

Classes (iCal) - next class is marked with N

    
    Tuesday
    04.10.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    11.10.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    18.10.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    25.10.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    08.11.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    15.11.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    22.11.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    29.11.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    06.12.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    13.12.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    10.01.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    17.01.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    24.01.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.Stock
    
    Tuesday
    31.01.
    15:00 - 16:30
    Hörsaal 13  Oskar-Morgenstern-Platz 1  2.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 a written exam on theoretical topics 1 and 2 on 22.11.2022, as well as three exercise sheets with programming part on topic 3 (provided on 06.12.2022, 10.01.2023, 24.01.2023). The exercise sheets should be submitted in a week and will be discussed in the exercise session.

Minimum requirements and assessment criteria

40 points for the written exam on topics 1 and 2
20 each exercise sheet

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: Tu 27.09.2022 12:48