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Empirical Bayes and Generalized Linear Mixed Models
Provider: Faculty of Science

Activity no.: 5539-23-07-31 
Enrollment deadline: 24/04/2023
PlaceDepartment of Mathematical Sciences
Universitetsparken 5, 2100 København Ø
Date and time24.04.2023, at: 08:00 - 23.06.2023, at: 16:00
Regular seats50
ECTS credits7.50
Contact personNina Weisse    E-mail address: weisse@math.ku.dk
Enrolment Handling/Course OrganiserJostein Paulsen    E-mail address: jostein@math.ku.dk
Written languageEnglish
Teaching languageEnglish
Semester/BlockBlock 4
Scheme groupC
Exam requirementsTo be allowed to take the exam two compulsory homeworks must be approved. These can be done in groups.
Exam formWritten examination
Exam detailsWritten examination, 3 timer under invigilation
Exam aidsAll aids allowed
Grading scale7 point grading scale. For PhD students: Passed / Not Passed
Exam re-examinationAs for the ordinary exam
Course workload
Course workload categoryHours
Lectures28.00
Preparation152.00
Exercises14.00
Exam12.00
Sum206.00
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).

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.

Literature
Notes written by the lecturer

Teaching and learning methods
4 hours of lectures and 2 hours of exercises a week for 7 weeks

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