Generalized Linear Models

Code:

M6024

Acronym:

M6024

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2024/2025 - 1S

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Doctoral Program in Applied Mathematics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
PDMATAPL	0	Official Study Plan	1	-	3	21	81

Teaching language

Suitable for English-speaking students

Objectives

1. Training for regression analysis involving responses 
following a distribution belonging to the exponential 
family (generalized linear models)

2. Understanding the estimation processes used in 
generalized linear models

3. Implementation of statistical analysis corresponding 
to the models studied in an appropriate software

4. Promotion of the student's critical spirit and 
autonomy

Learning outcomes and competences

At the end of the course, students are expected to:

a) acquire knowledge about statistical inference in generalized linear models

b) know how to choose correctly the statistical models learned to concrete problems

c) know how to apply and implement the models studied in R

d) acquire a critical spirit and the ability to interpret the results obtained.

Working method

Presencial

Program

1. Review of linear models.
2. Introduction to generalized linear models.
3. Estimation of the model parameters, hypothesis testing and confidence intervals.
4. Selection and validation of models.
5. Regression models for binary data.
6. Regression models for count data.
7. Regression models for skewed data

Mandatory literature

P. McCullagh; Generalized linear models. ISBN: 0-412-31760-5
Ludwig Fahrmeir; Multivariate statistical modelling based on generalized linear models. ISBN: 0-387-95187-3

Complementary Bibliography

Peter J. Green; Nonparametric regression and generalized linear models. ISBN: 9780412300400

Teaching methods and learning activities

Theoretical-practical classes with a significant 
theoretical component but simultaneously addressing 
different examples of application of techniques and 
statistical models presented in a computer laboratory. 
The used software will be R.

Software

R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Participação presencial	0,00
Apresentação/discussão de um trabalho científico	50,00
Trabalho escrito	50,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Apresentação/discussão de um trabalho científico	10,00
Estudo autónomo	50,00
Frequência das aulas	21,00
Total:	81,00

Eligibility for exams

Não há falta de frequência.

Calculation formula of final grade

The student will have to carry out two scientific works:
one on objectives 1. to 4. above and another on the
remaining objectives. There will be an oral presentation
and discussion of each work. Each work will be priced at
10 points and the student's final classification will be
the arithmetic average of the marks obtained in the two
scientific works.

Classification improvement

Th student will only be allowed to improve one of the two scientific works/projects.

Observations

Jury: Rita Gaio (UPorto) and Arminda Manuela Gonçalves (UMinho)