Course
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 glm’s 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.
Dette er emnebeskrivelsen for studieåret 2015-2016. Merk at det kan komme endringer.
Semesters
Fakta
Emnekode
STA600
Vekting (stp)
10
Semester undervisningsstart
Spring
Undervisningsspråk
English
Antall semestre
1
Vurderingssemester
Spring
Content
Learning outcome
After having completed the course one the student should:
Know the main theory for Generalized Linear Models (glm)
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, and
Be able to use glms in practical use of real data.
Forkunnskapskrav
Mathematical Methods 1 (MAT100)
Mathematical Methods 2 (MAT200)
Probability and Statistics 1 (STA100)
Probability and Statistics 2 (STA500)
Exam
| Form of assessment | Weight | Duration | Marks | Aid | Exam system | Withdrawal deadline | Exam date |
|---|---|---|---|---|---|---|---|
| Written exam | 1/1 | 4 Hours | Letter grades | None permitted | — | — | — |
Vilkår for å gå opp til eksamen/vurdering
Fagperson(er)
Head of Department:
Bjørn Henrik AuestadCourse coordinator:
Arild BulandCourse coordinator:
Tore Selland KleppeCourse coordinator:
Jörn SchulzMethod of work
Åpent for
Emneevaluering
Litteratur
Annette J. Dobson: An introduction to generalized linear models, second ed.
Ch.: 1 11.
Alternative books will be considered. If another book is chosen information about this will be given at the start of the term.