PřF:M9750 Robust and nonpar stat - Course Information
M9750 Robust and nonparametric statistical methodsFaculty of Science
- Extent and Intensity
- 2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
- 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
- Wed 8:00–9:50 M6,01011
- Timetable of Seminar Groups:
- 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;
- Introduction to robust and nonparametric methods.
- 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.
- 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
- Further Comments
- Study Materials
The course is taught annually.