Topics in Non-Life Insurance (Skade2)
Various distributions that are useful in non-life insurance
The impact of deductibles on the insurance premium calculations
Maximum likelihood methods, including the adjustment for deductibles. Also use of the delta method.
Asymptotic properties of the estimators
Model selection methods
Some linear and generalized linear model theory
Course description: Distributions in insurance, deductibles, maximum likelihood estimation, model choice, non parametric method, linear and generalized linear models.
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
Understand basic claim number and claim frequency models.
Understand the impact of deductibles, and how to compensate for this in modelling.
Know how to use maximum likelihood to fit insurance data.
Have some insight into the art of picking relevant and useful models for the data.
Know something about the consequences of picking a poor model
A good understanding of linear and generalized linear models, and the difference between continuous and discrete models.
Know how to fit nonstandard models, such as Pareto regression with deductibles.
Be able to take raw data and prepare for statistical analysis.
Be able to fit and select a meaningful model among a variety of alternatives. This includes picking good distributions, good covariates and avoid overfitting.
Run, and if necessary program in R, the necessary calculations.
Be able to go from raw data to practical and useful statistical models. A typical application is in pricing insurance products.
5 hours of lectures and 2 hours of excercises each week for 7 weeks.
Bachelor in insurance mathematics or similar.
Academic qualifications equivalent to a BSc degree is recommended.
- 7,5 ECTS
- Type of assessment
Written examination, 3 hours under invigilation
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
Criteria for exam assessment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.
Single subject courses (day)
- Theory exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 4
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Jostein Paulsen (7-6f7478796a6e73457266796d33707a336970)
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