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CA  >  Actuarial Science  >  Currículo  >  Risk Models

Master in Actuarial Science

Plano Curricular Actuarial Science

Risk Models (MR-CA)

UC Competência

Risk Models(Matemática)

UC Execução

Risk Models (2020/2021 - Semestre 1)
Risk Models (2019/2020 - Semestre 1)
Risk Models (2018/2019 - Semestre 1)
Modelos de Risco (2017/2018 - Semestre 1)
Modelos de Risco (2016/2017 - Semestre 1)
Modelos de Risco (2015/2016 - Semestre 1)
Modelos de Risco (2014/2015 - Semestre 1)
Modelos de Risco (2013/2014 - Semestre 1)
Modelos de Risco (2012/2013 - Semestre 1)
Modelos de Risco (2011/2012 - Semestre 1)


Grupo: Actuarial Science > 2º Ciclo > Unidades Curriculares Obrigatórias

Período: 1 Ano, 1 Semestre


6.0 (para cálculo da média)


The student is expected:
- To use statistical methods to define and estimate models adequate to model claims behaviour or other relevant aspects of the actuarial work.
- To understand the assumptions implicit in each statistical technique.
- To recognize which assumptions and statistical technique are appropriate to solve a given problem.


- Review of Basic statistical concepts
- Non-parametric estimation
- Frequentist estimation
- Bayesian estimation
- Model Selection
- Simulation and Bootstrap

Metodologia de avaliação

The curricular unit will be taught by mean of theoretical-practical lectures using slides to underline the main points and using a computer to solve some examples. Student's autonomous work is a main point of teaching methodologies. Students must also solve a set of exercises. The final grade, on the scale of 0 to 20, is assigned on the basis of a written exam (70%) and an exam using the computer (30%) based on EXCEL and R.



Loss Models ? From data to decisions

Klugman, S.A., Panjer, H.H. and Willmot, G.E.


4th Edition, John Wiley & Sons, Inc., New-Jersey.

Bootstrap Methods and Permutation Tests

Hesterberg, T., Monaghan, S., Mooree, D.S., Clipson, A., Epstein, R.


companion chapter 18 to The practice of Business Statistics by David S. Moore, MCCabe, Duckworth and Sclove.

Statistical Inference

Casella, G. and Berger, R.


(Second Edition). Duxbury Press.

An Introduction to the Bootstrap

Efron, B. and Tibshirami, R.J.


Chapman & Hall, New-York.


Ross, S.M.


3rd Edition, Academic Press

Applied Simulation Modeling

Seila, A., Ceric,V. and Tadikamalla,P.


Duxbury Applied Serie.

All of Statistics: A Concise Course in Statistical Inference

Wasserman, L.


New York, Springer.


Não existem referências bibliográficas secundárias.