M9750 Robust and nonparametric statistical methods

Faculty of Science
Autumn 2019
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Radim Navrátil, Ph.D. (lecturer)
Guaranteed by
doc. PaedDr. RNDr. Stanislav Katina, Ph.D.
Department of Mathematics and Statistics - Departments - Faculty of Science
Supplier department: Department of Mathematics and Statistics - Departments - Faculty of Science
Timetable
Wed 8:00–9:50 M6,01011
  • Timetable of Seminar Groups:
M9750/01: Wed 10:00–11:50 MP2,01014a, R. Navrátil
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
Robust statistical methods, unlike the classical ones, work also in a neighbourhood of normal distribution. The course is an introduction to robust estimates and rank tests. Students will meet basic estimates for location and linear regression. In the second part, rank tests as an alternative to classical tests will be introduced. On computer exercises students will learn to apply presented methods on real data.
Learning outcomes
After completing this course students will be able to:
- decide about model suitability;
- verify normality of data;
- use robust estimates in situation where classical methods fail;
- apply rank test on non-normal data;
Syllabus
  • Introduction to robust and nonparametric methods.
  • Robustness.
  • Robust estimates of the location: M-, L- and R- estimates.
  • Robust estimates in regression.
  • Ranks, theory of rank tests.
  • One and two-sample rank tests.
  • Rank test of independence.
  • Rank tests in regression.
Literature
  • JUREČKOVÁ, Jana. Robust Statistical Methods with R. Boca Raton, Florida, USA: Chapman&Hall/CRC, 2006. xi + 197. ISBN 13: 978-1-58488-4541. info
  • HÁJEK, Jaroslav, Zbyněk ŠIDÁK and Pranab Kumar SEN. Theory of rank tests. 2nd ed. San Diego: Academic Press, 1999. xiv, 435. ISBN 0126423504. info
  • JUREČKOVÁ, Jana. Pořadové testy. 1981. info
Teaching methods
Lectures - gaing knowledge of robust and nonparametric statistics. Exercises - practical use of the methods in statistical software (R, Excel).
Assessment methods
Oral exam - 50% of correct answers and correctly solved project are needed to pass.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms autumn 2017, Autumn 2018, Autumn 2020.
  • Enrolment Statistics (Autumn 2019, recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2019/M9750