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After successful completion of this course students will be able to:
- Understand the theory and motivation behind Generalized Linear Models (GLMs), state the basic components of a GLM and apply GLMs to actuarial problems;
- understand the theory and motivation behind IBNR models, list some commonly used IBNR models and understand their connection to GLMs and apply them to actuarial problems;
- understand the basics of credibility theory and its applications to actuarial problems;
- implement the studied concepts and techniques in R.
In this course many statistical techniques that can be applied in non-life insurance are studied. The first is Generalized Linear Models. GLMs are regression models in which the error is allowed, for example Poisson or gamma instead of Gaussian, and in which the volatility may depend on the mean. Such models have many actuarial applications. Also IBNR models are studied. Their purpose is to predict future payments on claims regarding events that have occurred in the past but are not yet (fully) known to the insurer. Another topic is the credibility theory which can be described as using least squares theory to predict claims for sectors of a portfolio, using both sector and company data. Apart from the theory the implementation of the techniques in R is studied and practised.
The student might want to refresh his/her knowledge about basic linear regression as well as the material in Ch. 1-4+6 of the textbook Modern Actuarial Risk Theory-Using R. Basic knowledge about probability & statistics as well as a working knowledge of the software R, are expected beforehand.
Three-hour lectures and three-hour computer classes each week.