Due to the COVID-19 crisis, the information below is subject to change,
in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
4 credits
15.0 h + 5.0 h
Q1
Teacher(s)
Desmet Lieven (compensates Legrand Catherine); Legrand Catherine;
Language
French
Main themes
- Review of generalised linear models - Dispersion models - Linear mixed models. - Generalised linear mixed models. - Autoregressive models. - Marginal models and generalised estimating equations.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
This is a second cycle course giving a critical overview of recent scientific developments in the field. It will deal with present extensions of linear and generalised linear models. The considered extensions will be of two types : - a explicit modelling of dispersion as a function of available covariates. - a amendment of (generalised) linear models to deal with clustered or longitudinal data. |
Bibliography
Transparents du cours disponible sur Moodle.
Références données au cours.
Références données au cours.
Teaching materials
- transparents sur moodle
Faculty or entity
LSBA
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Statistic: General
Master [120] in Statistic: Biostatistics
Master [120] in Biomedical Engineering