Universität Wien

040772 UK Complex Statistical Methods (2021W)

4.00 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

  • Tuesday 05.10. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 12.10. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 19.10. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 09.11. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 16.11. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 23.11. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 30.11. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 07.12. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 14.12. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 11.01. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 18.01. 13:15 - 14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Tuesday 25.01. 13:15 - 14:45 Hörsaal 8 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 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, 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 at the end of the course and three exercises sheets to each topic.
Exercise sheets with both theoretical and programming exercises will be provided at 02.11.2021, 30.11.2021 and 11.01.2022. Two weeks later the exercise sheets should be submitted and will be discussed in the exercise session.

Minimum requirements and assessment criteria

40 points oral exam
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

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 18.10.2021 13:27