Course - Generalized Linear Models - TMA4315
Generalized Linear Models
Choose study yearAbout
About the course
Course content
Univariate exponential family. Multiple linear regression. Logistic regression. Poisson regression. General formulation for generalised linear models with canonical link. Likelihood-based inference with score function and expected Fisher information. Deviance. AIC. Wald, score and likelihood-ratio test. Linear mixed effects models with random components of general structure. Random intercept and random slope. Generalised linear mixed effects models. Strong emphasis on programming in R. Possible extensions: quasi-likelihood, over-dispersion, models for multinomial data, analysis of contingency tables, quantile regression.
Learning outcome
1. Knowledge. The student can assess whether a generalised linear model can be used in a given situation and can further carry out and evaluate such a statistical analysis. The student has substantial theoretical knowledge of generalised linear models and associated inference and evaluation methods. This includes regression models for normal data, logistic regression for binary data and Poisson regression. The student has theoretical knowledge about linear mixed models and generalised linear mixed effects models, both concerning model assumptions, inference and evaluation of the models. Main emphasis is on normal, binomial and Poisson models with random intercept and random slope. 2. Skills. The student can assess whether a generalised linear model or a generalised linear mixed model can be used in a given situation, and can further carry out and evaluate such a statistical analysis.
Learning methods and activities
Lectures, exercises and works (projects).
Further on evaluation
Retake of examination may be given as an oral examination. The re-take exam will be held in August.
Students are free to choose Norwegian or English for written assessments.
Students that want to improve their grade in the course, can choose to retake one of the two evaluations. If the evaluation is changed, the whole course must be retaken.
Recommended previous knowledge
TMA4267 Linear Statistical Models or TMA4255 Applied Statistics. Good knowledge in R, a software environment for statistical computing and graphics.
Course materials
Will be announced at the beginning of the semester.
Other pages about the course
Subject areas
- Statistics
- Technological subjects