Generalized Linear Models (STA600)
Introduction to glm, which is a generalization of (multiple) regression for normally distributed responses to responses from a larger class of distributions, especially discrete responses. Theory for glms with application to regression for normally distributed data, logistic regression for binary and multinomial data; Poisson regression and survival analysis. Applications to data, principles of statistical modeling, estimation and inference are emphasized. Likelihood theory.
Course description for study year 2023-2024
Facts
Course code
STA600
Version
1
Credits (ECTS)
10
Semester tution start
Spring
Number of semesters
1
Exam semester
Spring
Language of instruction
English
Time table
Content
Introduction to generalized linear models (GLM), which is a generalization of (multiple) regression for normally distributed responses to responses from a larger class of distributions, especially discrete responses. Theory for GLMs with application to among other tings, regression for normally distributed data, logistic regression for binary and multinomial data; Poisson regression and survival analysis. Principles of statistical modeling, likelihood theory, estimation and inference, bayesian methods. Applications and analyses of data sets are emphasized.
Learning outcome
After having completed the course one the student should:
- Know the main theory for generalized linear models
- Know how regression with binary, multinomial, Poisson- and survival time responses may be done
- Understand use of likelihood estimation generally and especially for generalized linear models
- Be able to apply the theory in practical use on real data.
Required prerequisite knowledge
MAT100 Mathematical Methods 1, MAT200 Mathematical Methods 2, STA100 Probability and Statistics 1
or equivalent courses.
Recommended prerequisites
STA500 Probability and Statistics 2
Exam
Form of assessment | Weight | Duration | Marks | Aid |
---|---|---|---|---|
Oral exam | 1/1 | 45 Minutes | Letter grades | None permitted |
Coursework requirements
Two compulsory assigned exercises
Mandatory assignments must be passed for the student to have admittance to the exam.
Course teacher(s)
Course coordinator:
Jörn SchulzCourse coordinator:
Arild BulandCourse coordinator:
Tore Selland KleppeHead of Department:
Bjørn Henrik AuestadMethod of work
4 hours lectures and 2 hours problem solving per week.
Open for
Course assessment
There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.