At the end of this course the student will have acquired the knowledge, skills and competences that will allow him:
- To be able to create distributions through core operations
- To know the main distributions and their relationships used for the insurer’s claims
- To analyze the tail of a distribution and know how to classify it
- To be able to understand the basic concepts of extreme theory applied to actuarial sciences
- To know how to use statistical techniques for typical data concerning the number and amount of claims
Pedro Alexandre da Rosa Corte Real
Weekly - 4
Total - 70
- Bowers, Newton, Gerber, Hickman, Jones and Nesbitt. (1997) Actuarial Mathematics (second edition). Itasca, Illinois: The Society of Actuaries.
- Dickson, D.C.M., Hardy, M.R. and Waters, H.R.. Actuarial Mathematics for Life Contingent Risks. Cambridge University Press, 2013.
- Kaas, R., Goovaerts, M., Dhaene, J. & Denuit, M. (2008) Modern Actuarial Risk Theory - using R (second edition), Springer.
- Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012) Loss Models: From Data To Decisions (fourth edition), Wiley.
In the theoretical and practical lectures, it will be explained and discussed the successive topics of the course program. The topics are introduced by the teacher, consolidated using as much as possible with real examples drawn from the insurance industry, followed by a brief discussion and use of computational means to support problem solving.
Two tests, to be carried out during the academic period and exam (s) according to the academic calendar.
Each of the tests has a weight of 50% for the calculation of the final grade, being exempt from examination the student who has a weighted average greater than or equal to 9.5, with both tests evaluated at least 7.5 values.
Presential tests and exams, with consultation of all paper-based material that students want to use. If there is a component that requires computer support, students will be able to use their pc or pc in the laboratory, without access to the internet.
1 Sum of independent random variables
1.1 Some results
2 Creating new distributions
2.1 Multiplication by a constant
2.2 Raising to a power and exponentiation
3 Distribution families
3.1 Parametric families
3.2 Limiting distributions
3.3 Relationships between distributions
3.4 Exponential family
4 Tails of distributions
4.2 Equilibrium distribution
4.3 Tail behavior
5 Extreme value distributions
5.1 Distribution of the maximum
5.2 Maximum domain of attraction
5.3 Generalized Pareto distribution
5.4 Limiting distributions of excesses
6.1 Kaplan-Meier estimator
6.2 Nelson-Aalen estimator
6.3 Kernel density models
6.4 Estimation to complete data
6.5 Estimation to modified data
6.6 Estimation to truncated data
Programs where the course is taught: