Universität Wien

040772 UK Complex Statistical Methods (2020W)

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. 17 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 07.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 14.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 21.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 28.10. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 04.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 11.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 18.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 25.11. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 02.12. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 09.12. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 16.12. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 13.01. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Wednesday 20.01. 15:00 - 16:30 Seminarraum 16 Oskar-Morgenstern-Platz 1 3.Stock
  • Tuesday 26.01. 18:30 - 20:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

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

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

Methods:
Lecture with exercise sessions.
Lecture notes and data will be available online.
Students are supposed to code in statistical software.

Assessment and permitted materials

There is an oral exam and three programming exercises to each topic that should be solved in presence.

Minimum requirements and assessment criteria

The final grade will be weighted as follows:
25% oral exam
25% each programming exercise

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 modelling and its applications.
Randall L. Eubank. (1999) Nonparametric regression and spline smoothing.

Association in the course directory

Last modified: Mo 28.09.2020 15:27