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CA  >  Actuarial Science  >  Currículo  >  Generalized Linear Models

Mestrado em Actuarial Science

Plano Curricular Actuarial Science


Generalized Linear Models (MLG)

UC Competência

Generalized Linear Models(Matemática)

UC Execução

Generalized Linear Models (2020/2021 - Semestre 2)
Generalized Linear Models (2019/2020 - Semestre 2)
Generalized Linear Models (2018/2019 - Semestre 2)
Modelos Lineares Generalizados (2017/2018 - Semestre 2)
Modelos Lineares Generalizados (2016/2017 - Semestre 2)
Modelos Lineares Generalizados (2015/2016 - Semestre 2)
Modelos Lineares Generalizados (2014/2015 - Semestre 2)
Modelos Lineares Generalizados (2013/2014 - Semestre 2)
Modelos Lineares Generalizados (2012/2013 - Semestre 2)
Modelos Lineares Generalizados (2011/2012 - Semestre 2)

Contextos

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

Período: 1 Ano, 2 Semestre

Peso

4.0 (para cálculo da média)

Objectivos

To introduce the foundations of Generalized Linear Models (GLM) and its applications. Provide skills for real-data estimation of GLM. Additionally, to introduce the main concepts of machine learning and some key algorithms. Provide skills to use appropriate software to apply machine learning techniques to simple real-data problems.

Programa

Generalized Linear Models:
- Review of linear regression models
- Generalized linear models: general overview
- Inference
- Examples of generalized linear models for continuous and discrete response
- Quasi-likelihood and overdispersion
Machine Learning:
- What is machine learning
- Branches of machine learning
- Applications

Metodologia de avaliação

Lectures will alternate theoretical presentations of statistical models with data analysis performed with suitable software.
The final grade, on a 0-20 scale, is awarded on the basis of a written exam and of a practical exam done on a computer using R. The mark on the written exam will be worth 70% of the final grade.

Bibliografia

Principal

Generalized Linear Models

McCullagh P. And Nelder, J.A.

1989

2nd Edition, Chapman and Hall, London.

Statistical Inference ? Based on the Likelihood

Azzalini A.

1996

Chapman and Hall

Modern Applied Statistics with S

Venables W. N. and Ripley B. D

2002

4th Edition, Spinger

Secundária

Applying Generalized Linear Models

Lindsey, J.K.

1997

Springer-Verlag, New York.

Introduction to S-Plus for Generalized Linear Modelling

Altham, P.M.E.

2006