Universität Wien

040772 UK Complex Statistical Methods (MA) (2025W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work
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

  • Tuesday 07.10. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 14.10. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 21.10. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 28.10. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 04.11. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 11.11. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 18.11. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 25.11. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 02.12. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 09.12. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 13.01. 13:15 - 14:45 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 20.01. 13:15 - 14:45 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course

Aims:
Get acquainted with concepts of nonparametric density estimation and regression: methodology, theory, and applications.
Implementation of methods in statistical software is part of the lecture.

Contents:
1. Histograms and kernel density estimators
2. Local polynomial estimators
3. Spline estimators

Methods:
Lectures with exercise sessions.
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 for the theoretical part on 20.01.2026 and three exercise sheets for each topic.
Exercise sheets with both theoretical and programming exercises have to be submitted on 02.11.2025, 25.11.2025, and 13.01.2026.
Each student should present at least one problem to get the points.

The use of AI tools (e.g. ChatGPT) for the production of texts is only permitted if they are expressly requested by the course leader (e.g. for individual work tasks).

Minimum requirements and assessment criteria

40 points written exam
20 each exercise sheet
The grade results according to the scheme: 4 from 50 points, 3 from 63 points, 2 from 75 points, and 1 from 87 points.

Examination topics

All topics covered in the lecture.

Reading list

Tsybakov. A. (2009) Introduction to nonparametric estimation.
Fan, J. and Gijbels, I. (1996) Local polynomial modeling and its applications.
Randall L. Eubank. (1999) Nonparametric regression and spline smoothing.

Association in the course directory

Last modified: We 13.08.2025 11:05