Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
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. |
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
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 Biomedical Engineering
Master [120] in Statistic: Biostatistics
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Statistic: General
Master [120] in Data Science : Statistic