Program
- This is a course of the Master of Biostatistics
Some important facts
- It is assumed that you have some experience with statistical concepts, such as, independence, estimators, likelihood principle, multiple linear regression model, hypothesis tests.
- In this course we will write programs in the language R. But, if you code in another language, for instance, Python or Matlab, it is also fine.
- Each session have a duration of two hours.
General Objective
To estimate, check and compare generalized linear models.
Topics
In this course we will cover the following topics:
Fundamentals
Link Functions
Fitting a GLM
Comparing Models
Models for Asymmetric Positive Data
Models for Gamma Response
Models for Inverse Normal Response
Models for Binary Data
Logit Model
Probit Model
Models for Count Data
Models for Poisson Response
Models for Negative Binomial Response
Grading
This course will be grade based on an applied project. The weigths of each stage of the project are:
- 29/02 Formulation of the project 10%
- 28/03 First report 15%
- 25/05 Second report 15%
- 20/06 Final paper 50%
- 04/07 Oral exposition 10%
The Final paper must have a minimum of eigth pages and a maximum of twelve pages.
Bibliography
- A. Agresti (2015) Foundations of Linear and Generalized Linear Models. Wiley.
- Casella G. and Berger, (2000). Statistical Inference, Second Edition.
- A. Dobson and A. Barnett (2017) An introduction to Generalized Linear models. Fourth Edition. CRC
- J. Faraway (2017) Extending the Linear Model with R. Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC
- G. A. Paula (2013) Modelos de Regressao: com apoio computacional (in portuguese).
- R Core Team, R: A language and environment for statistical computing, R Foundation for Statistical Computing, 2019.
- S. Wood (2006) Generalized Additive Models: An Introduction with R. Second edition. Chapman and Hall.