M8751 Advanced Regression Models I

Faculty of Science
spring 2018
Extent and Intensity
2/2. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
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
Wed 18:00–19:50 M2,01021
  • Timetable of Seminar Groups:
M8751/01: Fri 10:00–11:50 MP1,01014
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
  • Nonlinear parametric regression models
  • Regression with correlated data, generalized least squares, generalized estimating equations
  • Model selection, regularization techniques
  • Regression models in survival analysis
Literature
  • VERBEKE, Geert and Geert MOLENBERGHS. Linear mixed models for longitudinal data. New York: Springer-Verlag, 2009. xxii, 568. ISBN 9781441902993. info
  • KLEIN, John P. and Melvin L. MOESCHBERGER. Survival analysis : techniques for censored and truncated data. 2nd ed. New York: Springer, 2003. xv, 536. ISBN 9781441929853. 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
  • MOLENBERGHS, Geert and Geert VERBEKE. Models for discrete longitudinal data. New York: Springer-Verlag, 2005. ISBN 978-0-387-28980-9. info
  • 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
Teaching methods
Lectures, exercises
Assessment methods
Oral examination, homework assignments
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Teacher's information
https://is.muni.cz/auth/el/1431/jaro2018/M8751/index.qwarp
The course is also listed under the following terms Spring 2017, Spring 2019, Spring 2020, Spring 2021, Spring 2022.
  • Enrolment Statistics (spring 2018, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2018/M8751