M7222 Generalized linear models

Faculty of Science
Autumn 2024
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
Mgr. Tomáš Pompa (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 8:00–9:50 M5,01013
  • Timetable of Seminar Groups:
M7222/01: Thu 18:00–19:50 MP1,01014, T. Pompa
M7222/02: Mon 12:00–13:50 MP1,01014, T. Pompa
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponential families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
  • FAHRMEIR, Ludwig, Thomas KNEIB, Stefan LANG and Brian D. MARX. Regression : models, methods and applications. Berlin: Springer, 2013, xiv, 698. ISBN 9783642343322. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Semestral data project, written final exam, possibly with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2024/M7222/index_rsllz.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023.

M7222 Generalized linear models

Faculty of Science
Autumn 2023
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
Mgr. Markéta Makarová (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 14:00–15:50 M2,01021
  • Timetable of Seminar Groups:
M7222/01: Thu 8:00–9:50 MP1,01014, M. Makarová
M7222/02: Thu 16:00–17:50 MP1,01014, M. Makarová
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponential families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
  • FAHRMEIR, Ludwig, Thomas KNEIB, Stefan LANG and Brian D. MARX. Regression : models, methods and applications. Berlin: Springer, 2013, xiv, 698. ISBN 9783642343322. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Semestral data project, written final exam, possibly with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2023/M7222/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2022
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
doc. Mgr. David Kraus, Ph.D. (lecturer)
doc. PaedDr. RNDr. Stanislav Katina, 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
Wed 16:00–17:50 M3,01023
  • Timetable of Seminar Groups:
M7222/01: Wed 18:00–19:50 MP1,01014, K. Hrabcová
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponential families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Semestral data project, written final exam, possibly with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2022/M7222/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
autumn 2021
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
doc. PaedDr. RNDr. Stanislav Katina, 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 14:00–15:50 M6,01011
  • Timetable of Seminar Groups:
M7222/01: Mon 12:00–13:50 MP1,01014, A. Kraus
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponential families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Semestral data project, written final exam, possibly with a bonus for an optional written midterm exam. The form of the exam will be adjusted to the epidemiological situation preceding the date of the exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2021/M7222/index.qwarp
https://is.muni.cz/auth/el/sci/podzim2021/M7222/index-KFhemy.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2020
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., 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 M5,01013
  • Timetable of Seminar Groups:
M7222/01: Wed 16:00–17:50 MP1,01014, A. Kraus
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponential families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused on in-depth understanding of the theory and on practical data analysis
Assessment methods
Semestral data project, written and oral final exam, possibly with a bonus for an optional written and oral midterm exam. The form of the exam will be adjusted to the epidemiological situation preceding the date of the exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2020/M7222/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2019
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., 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
Fri 8:00–9:50 M5,01013
  • Timetable of Seminar Groups:
M7222/01: Mon 12:00–13:50 MP1,01014, A. Kraus
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponential families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused primarily on data analysis
Assessment methods
Semestral data project, written final exam, possibly with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/auth/el/sci/podzim2019/M7222/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2018
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., 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. Thu 16:00–17:50 M6,01011
  • Timetable of Seminar Groups:
M7222/01: Mon 17. 9. to Fri 14. 12. Fri 10:00–11:50 MP1,01014, A. Kraus
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponential families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused primarily on data analysis
Assessment methods
Semestral data project, written final exam, possibly with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/el/1431/podzim2018/M7222/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
autumn 2017
Extent and Intensity
2/2/0. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
Mgr. Andrea Kraus, M.Sc., Ph.D. (lecturer)
Mgr. Veronika Horská, Ph.D. (assistant)
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. Thu 8:00–9:50 M1,01017
  • Timetable of Seminar Groups:
M7222/01: Mon 18. 9. to Fri 15. 12. Fri 8:00–9:50 MP1,01014, A. Kraus
Prerequisites
Probability and mathematical statistics, in particular theory of estimation and testing statistical hypotheses: at the level of the course M4122. Statistical software R: at the level of the course M4130. Linear models: at the level of the course M5120.
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 introduces generalized linear models as a generalization of the linear model for situations, where the assumption of normality, linearity and/or homoskedasticity is violated. The course covers theory, software implementation, applications and interpretation, and lays foundations for the study of more advanced regression models.
Learning outcomes
After the course, the students
- are able to recognize the situations that can be addressed by generalized linear models;
- are able to choose, formulate and implement an appropriate model from this class, and interpret the results;
- to this aim, the students develop a deeper understanding of the theory of modelling, estimation and testing than what is sufficient for linear models;
- at the same time, the students are made aware of the limitations of generalized linear models and are offered an overview of the models that can be used in situations that cannot be addressed by generalized linear models.
Syllabus
  • Overview.
  • Exponencial families of distributions.
  • Maximum likelihood and quasi-likelihood.
  • Theory and practice of estimation in generalized linear models.
  • Deviance and residuals, and their role in model diagnostics and model selection.
  • Logistic regression, generalizations for multicategory response, applications in classification.
  • Poisson and multinomial regression, contingency tables.
  • Generalized linear models for continuous response.
Literature
    recommended literature
  • WOOD, Simon N. Generalized additive models : an introduction with R. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2017, xx, 476. ISBN 9781498728331. info
  • FARAWAY, Julian James. Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models. Second edition. Boca Raton, FL: CRC Press/Taylor & Francis Group, 2016, xiii, 399. ISBN 9781498720960. info
  • AGRESTI, Alan. Categorical data analysis. 2nd ed. Hoboken: John Wiley & Sons, 2002, xv, 710. ISBN 0471360937. info
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
    not specified
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: exercises focused primarily on data analysis
Assessment methods
Semestral data project, written final exam, possibly with a bonus for an optional written midterm exam.
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
Teacher's information
https://is.muni.cz/el/1431/podzim2017/M7222/index.qwarp
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2016
Extent and Intensity
2/2. 4 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Mgr. Andrea Kraus, M.Sc., Ph.D. (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 19. 9. to Sun 18. 12. Tue 8:00–9:50 M2,01021
  • Timetable of Seminar Groups:
M7222/01: Mon 19. 9. to Sun 18. 12. Tue 10:00–11:50 M2,01021, M. Forbelská
Prerequisites
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the R software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Active participation in seminars (10%), independently developed homework assignments (30%), oral exam with written preparation (60%).
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2015
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 8:00–9:50 M6,01011
  • Timetable of Seminar Groups:
M7222/01: Wed 10:00–10:50 M6,01011, M. Forbelská
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the R software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Active participation in seminars (10%), independently developed homework assignments (30%), oral exam with written preparation (60%).
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2014
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 10:00–11:50 M2,01021
  • Timetable of Seminar Groups:
M7222/01: Mon 12:00–12:50 M2,01021
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the R software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Active participation in seminars (10%), independently developed homework assignments (30%), oral exam with written preparation (60%).
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2013
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 12:00–13:50 M2,01021
  • Timetable of Seminar Groups:
M7222/01: Thu 14:00–14:50 M2,01021
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the R software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Active participation in seminars (10%), independently developed homework assignments (30%), oral exam with written preparation (60%).
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2012
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
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 M3,01023
  • Timetable of Seminar Groups:
M7222/01: Thu 10:00–10:50 M3,01023
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the R software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Lecture with a seminar. Active work in seminars. Oral examination.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2011
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Mon 12:00–13:50 M3,01023
  • Timetable of Seminar Groups:
M7222/01: Mon 14:00–14:50 M3,01023
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the MATLAB software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Lecture with a seminar. Active work in seminars. Oral examination.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2010
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Wed 14:00–15:50 M5,01013
  • Timetable of Seminar Groups:
M7222/01: Wed 16:00–16:50 M5,01013
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the MATLAB software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Lecture with a seminar. Active work in seminars. Oral examination.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2009
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 8:00–9:50 M4,01024
  • Timetable of Seminar Groups:
M7222/01: Fri 10:00–10:50 M4,01024
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the MATLAB software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Lecture with a seminar. Active work in seminars. Oral examination.
Language of instruction
Czech
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2008
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
RNDr. Marie Forbelská, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 8:00–9:50 MP1,01014, Fri 8:00–9:50 M3,01023
  • Timetable of Seminar Groups:
M7222/01: Fri 10:00–10:50 MP1,01014, Fri 10:00–10:50 M3,01023
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the MATLAB software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Assessment methods
Lecture with a seminar. Active work in seminars. Oral examination.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2007
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
RNDr. Marie Forbelská, Ph.D.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 8:00–9:50 U1
  • Timetable of Seminar Groups:
M7222/01: Fri 10:00–10:50 U1
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models.
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course is aimed to generalized linear models which are lately frequently used for analysing real data in many branches such as in biology, medicine, sociology and others. The first part of the course is devoted to the theory of the generalized linear models then gamma, poisson, logistic regression and theory of log-linear models follow. Throughout the course the computer packages outputs are used to demonstrate real data processing.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, efficiency and Rao-Cramer lower bound, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2006
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Thu 7:00–8:50 UP1
  • Timetable of Seminar Groups:
M7222/01: Thu 10:00–10:50 M3,04005 - dříve Janáčkovo nám. 2a, Thu 10:00–10:50 N41
Prerequisites (in Czech)
M6120 Linear Models in Statistics II
Course Enrolment Limitations
The course is offered to students of any study field.
Language of instruction
Czech
Further Comments
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2005
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Tue 9:00–10:50 N41
  • Timetable of Seminar Groups:
M7222/01: Tue 11:00–11:50 N41, Tue 11:00–11:50 M3,04005 - dříve Janáčkovo nám. 2a, M. Forbelská
Prerequisites (in Czech)
M6120 Linear Models in Statistics II
Course Enrolment Limitations
The course is offered to students of any study field.
Language of instruction
Czech
Further Comments
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2004
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Timetable
Fri 7:00–8:50 B411
  • Timetable of Seminar Groups:
M7222/01: Fri 9:00–9:50 B411, M. Forbelská
Prerequisites (in Czech)
M6120 Linear Models in Statistics II
Course Enrolment Limitations
The course is offered to students of any study field.
Language of instruction
Czech
Further Comments
The course is taught annually.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2003
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
M6120 Linear Models in Statistics II
Course Enrolment Limitations
The course is offered to students of any study field.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2002
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
doc. RNDr. Jaroslav Michálek, CSc. (lecturer)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
M6120 Linear Models in Statistics II
Course Enrolment Limitations
The course is offered to students of any study field.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2011 - acreditation

The information about the term Autumn 2011 - acreditation is not made public

Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the MATLAB software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Lecture with a seminar. Active work in seminars. Oral examination.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2010 - only for the accreditation
Extent and Intensity
2/1. 3 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites
M6120 Linear Models in Statistics II
Basic knowledge of the theory of estimation and knowledge of linear statistical models of full rank (regression analysis) and not full rank (ANOVA).
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 aim of this course is to consider generalized linear models as a broad class of statistical models applying the general principles of likelihood inference to a variety of commonly encountered data analysis problems in many branches such as in biology, medicine, sociology and others. For computer labs the MATLAB software environment is used. Upon successful completion of the course students should be able to understand principles of parameter estimation and hypotheses testing in a generalized linear model; apply the methods to build models to address practical objectives; learn to interpret the results properly.
Syllabus
  • Selected topics of statistical estimation theory: family of regular densities, exponential family of distributions, maximal likelihood estimation and its properties. Theory of generalized linear models: generalization of classical linear regression model, construction of generalized linear model and its description, model fitting, minimal, maximal models, submodels, goodness-of-fit measures and residua, testing of adequacy of a model, diagnostics. Gamma regression, models for binary and binomial data, logistic regression, dose response models, models for nominal and ordinal data, Poisson regresion, log-linear models and contingency tables.
Literature
  • An introduction to generalized linear models. Edited by Annette J. Dobson. 2nd ed. Boca Raton: CRC Press, 2002, vii, 225 s. ISBN 1-58488-165-8. info
  • FAHRMEIR, Ludwig and Gerhard TUTZ. Multivariate statistical modelling based on generalized linear models. New York: Springer-Verlag, 1994, 425 s. ISBN 0387942335. info
Teaching methods
Lectures: theoretical explanation with practical examples
Exercises: solving problems for understanding of basic concepts and theorems, contains also more complex problems.
Assessment methods
Lecture with a seminar. Active work in seminars. Oral examination.
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2007 - for the purpose of the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.

M7222 Generalized linear models

Faculty of Science
Autumn 2007 - for the purpose of the accreditation
Extent and Intensity
2/1. 2 credit(s) (fasci plus compl plus > 4). Type of Completion: zk (examination).
Teacher(s)
RNDr. Marie Forbelská, Ph.D. (lecturer)
Guaranteed by
doc. RNDr. Jaroslav Michálek, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Prerequisites (in Czech)
M6120 Linear Models in Statistics II
Course Enrolment Limitations
The course is offered to students of any study field.
Language of instruction
Czech
Further Comments
The course is taught annually.
The course is taught: every week.
Listed among pre-requisites of other courses
The course is also listed under the following terms Autumn 2010 - only for the accreditation, Autumn 2002, Autumn 2003, Autumn 2004, Autumn 2005, Autumn 2006, Autumn 2007, Autumn 2008, Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2011 - acreditation, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, autumn 2021, Autumn 2022, Autumn 2023, Autumn 2024.
  • Enrolment Statistics (recent)