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PřF:M8751 Advanced Regression Models I - Course Information

## M8751 Advanced Regression Models I

**Faculty of Science**

Spring 2019

**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**- Mon 18. 2. to Fri 17. 5. Fri 8:00–9:50 M6,01011
- Timetable of Seminar Groups:

*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**- Statistics and Data Analysis (programme PřF, N-MA)

**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

- VERBEKE, Geert and Geert MOLENBERGHS.
**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

- Enrolment Statistics (Spring 2019, recent)
- Permalink: https://is.muni.cz/course/sci/spring2019/M8751