Empirical Bayes and Generalized Linear Mixed Models
Course content
Empirical Bayes methods; Conjugate families; Compound models with left truncated data; Linear mixed models (LMM); Generalized linear mixed models (GLMM); Hierarchical generalized linear mixed models (HGLM).
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
The aim of the class is to show how standard and not so standard statistical models can be extended to include random parameters. In insurance this is useful when policyholders can naturally be segmented into groups, so that policies within a group are not independent. For actual solutions of these models numerical integration is often required, but sometimes analytical results are available and we shall pursue some of these. During the course the students will use R programs with some data from insurance. Some more basic programming will also be required.
4 hours of lectures and 2 hours of exercises a week for 7 weeks
Notes written by the lecturer
Regression; Topics in non life insurance
- ECTS
- 7,5 ECTS
- Type of assessment
-
Written examination, 3 timer under invigilation
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
Criteria for exam assessment
Based on the written exam.
Single subject courses (day)
- Category
- Hours
- Lectures
- 28
- Preparation
- 152
- Exercises
- 14
- Exam
- 12
- English
- 206
Kursusinformation
- Language
- English
- Course number
- NMAK22003U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 block
- Placement
- Block 4
- Schedulegroup
-
C
- Capacity
- Unlimited
The number of seats may be reduced in the late registration period - Studyboard
- Study Board of Mathematics and Computer Science
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinator
- Jostein Paulsen (7-73787c7d6e727749766a7d7137747e376d74)
Teacher
Jostein Paulsen
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