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

Learning outcome

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

7,5 ECTS
Type of assessment
Written examination, 3 timer under invigilation
Type of assessment details
The course has been selected for ITX exam.
See important information about ITX-exams at Study Information, menu point: Exams -> Exam types and rules -> Written on-site exams (ITX).
All aids allowed

As the exam is an ITX-exam, the University will make computers available to students at the exam. Students are therefore not permitted to bring their own computers, tablets, calculators, or mobile phones.

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


Course number
7,5 ECTS
Programme level
Full Degree Master

1 block

Block 4

The number of seats may be reduced in the late registration period
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Jostein Paulsen   (7-6c717576676b70426f63766a306d7730666d)

Jostein Paulsen

Saved on the 10-11-2022

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