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).

Education

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

Collective
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

Saved on the 07-04-2022

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