Teacher(s)
Language
French
Prerequisites
Mastery of basic concepts in statistics and probability calculation, at the level of courses in the FSA1BA, INGE1BA, MATH1BA programs or the access minor in statistics, actuarial sciences and data science.
Main themes
Modeling claim propension, claim counts, claim severities and claim duration with generalized linear models (GLM), generalized additive models (GAM and GAMLSS), mixed models (GLMM) and generalized non-linear models (GNM).
Content
The course covers the following chapters:
- Segmentation in insurance
- Exponential dispersion models
- Maximum likelihood estimation
- Generalized linear models (GLM)
- Generalized additive models (GAM)
- Mixed models
- Generalized nonlinear models (GNM)
Teaching methods
The course consists of theoretical lessons illustrated with numerous practical cases, in which students are required to participate.
Evaluation methods
The evaluation consists of, on one hand, a written exam covering the theoretical course and practical sessions, for which the student has access to course materials (syllabus, slides, exercises, etc.), and on the other hand, a project to be submitted during the year.
Online resources
Moodle website
Bibliography
Denuit, M., Hainaut, D., Trufin, J. (2019). Effective Statistical Learning Methods for Actuaries. Volume 1: GLMs and their Extensions. Springer Actuarial Lecture Notes Series.
Faculty or entity
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Actuarial Science
