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 2024-2025
Course code
STA600
Version
1
Credits (ECTS)
10
Semester tution start
Spring
Number of semesters
1
Exam semester
Spring
Language of instruction
English
Content
NB! This is an elective course and may be cancelled if fewer than 10 students are enrolled by January 20th for the spring semester.
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
Recommended prerequisites
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Oral exam | 1/1 | 45 Minutes | Letter grades | None permitted |
Oral exam is individual.
