M8752 Advanced regression models II

Faculty of Science
autumn 2021
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Taught in person.
Teacher(s)
Mgr. David Kraus, 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
Tue 12:00–13:50 M2,01021
  • Timetable of Seminar Groups:
M8752/01: Thu 14:00–15:50 MP2,01014a, D. Kraus
Prerequisites
M7222 Generalized linear models
Calculus, linear algebra, basics of probability theory and mathematical statistics, theory of estimation and hypotheses testing, linear and generalized linear models, basic methods of time series analysis, knowledge of R software
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The course offers a coverage of selected advanced regression methods and models beyond linear and generalized linear regression. The couse covers theoretical foundations, statistical models and inference, software implementation, application and interpretation.
Learning outcomes
The students will gain a deeper understanding of the methods and their relations and learn to recognize situations that can be addressed by the models discussed in the course, choose an appropriate model, implement it and interpret the results.
Syllabus
  • Regression models in event history analysis
  • Linear mixed effects models
  • Generalized linear mixed effects models
  • Nonparametric and semiparametric regression, penalized splines, generalized additive models
  • Quantile regression
  • Experimental design, study planning
Literature
  • Survival and event history analysisa process point of view. Edited by Odd O. Aalen - Ørnulf Borgan - S. Gjessing. New York, NY: Springer, 2008. xviii, 539. ISBN 9780387202877. info
  • VERBEKE, Geert and Geert MOLENBERGHS. Linear mixed models for longitudinal data. New York: Springer-Verlag, 2009. xxii, 568. ISBN 9781441902993. info
  • MOLENBERGHS, Geert and Geert VERBEKE. Models for discrete longitudinal data. New York: Springer-Verlag, 2005. ISBN 978-0-387-28980-9. info
  • WOOD, Simon N. Generalized additive models : an introduction with R. Boca Raton, Fla.: Chapman & Hall/CRC, 2006. xvii, 392. ISBN 1584884746. info
  • HASTIE, Trevor, Robert TIBSHIRANI and J. H. FRIEDMAN. The elements of statistical learning : data mining, inference, and prediction. 2nd ed. New York, N.Y.: Springer, 2009. xxii, 745. ISBN 9780387848570. info
Teaching methods
Lectures, exercises
Assessment methods
Oral examination, homework assignments
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is also listed under the following terms autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/autumn2021/M8752