M8752 Advanced regression models II

Faculty of Science
Autumn 2024
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
RNDr. Bc. Iveta Selingerová, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. David Kraus, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 10:00–11:50 M2,01021
  • Timetable of Seminar Groups:
M8752/01: Tue 16:00–17:50 MP1,01014, I. Selingerová
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, autumn 2021, Autumn 2022, Autumn 2023.

M8752 Advanced regression models II

Faculty of Science
Autumn 2023
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
RNDr. Bc. Iveta Selingerová, Ph.D. (seminar tutor)
Guaranteed by
doc. Mgr. David Kraus, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 12:00–13:50 M6,01011
  • Timetable of Seminar Groups:
M8752/01: Thu 8:00–9:50 MP2,01014a, I. Selingerová
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
Study Materials
The course is taught annually.
The course is also listed under the following terms autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2024.

M8752 Advanced regression models II

Faculty of Science
Autumn 2022
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
Mgr. Karolína Hrabcová (seminar tutor)
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 16:00–17:50 M3,01023
  • Timetable of Seminar Groups:
M8752/01: Wed 16:00–17:50 MP2,01014a, K. Hrabcová
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
Study Materials
The course is taught annually.
The course is also listed under the following terms autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2023, Autumn 2024.

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).
Teacher(s)
doc. 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, Autumn 2022, Autumn 2023, Autumn 2024.

M8752 Advanced regression models II

Faculty of Science
Autumn 2020
Extent and Intensity
2/2/0. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. 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
Thu 8:00–9:50 M6,01011
  • Timetable of Seminar Groups:
M8752/01: Fri 10:00–11:50 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, 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 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M8752 Advanced regression models II

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)
doc. 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
Thu 16:00–17:50 M3,01023
  • Timetable of Seminar Groups:
M8752/01: Thu 18:00–19:50 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, 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 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M8752 Advanced regression models II

Faculty of Science
Autumn 2018
Extent and Intensity
2/2. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. 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 17. 9. to Fri 14. 12. Mon 18:00–19:50 M6,01011
  • Timetable of Seminar Groups:
M8752/01: Mon 17. 9. to Fri 14. 12. Wed 18:00–19:50 MP1,01014, 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, 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 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M8752 Advanced regression models II

Faculty of Science
autumn 2017
Extent and Intensity
2/2. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. 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. 9. to Fri 15. 12. Wed 16:00–17:50 M3,01023
  • Timetable of Seminar Groups:
M8752/01: Mon 18. 9. to Fri 15. 12. Thu 16:00–17:50 MP1,01014, 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, 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 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M8752 Pokročilé regresní modely II

Faculty of Science
Autumn 2016

The course is not taught in Autumn 2016

Extent and Intensity
2/2. 4 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. 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
Prerequisites
M7222_AKR
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. 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
  • Linear mixed effects models
  • Generalized linear mixed effects models
  • Nonparametric and semiparametric regression, generalized additive models
  • Analysis of longitudinal and spatially correlated data
Teaching methods
Lectures, exercises, practical project
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
The course is also listed under the following terms autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (recent)