Sumários
Lecture 6
17 Abril 2020, 08:00 • Rui Paulo
Inference in the context of a GLM: the iterative rewheighted least squares algorithm for estimating the coefficients of the linear predictor.
Estimation of the dispersion parameter.
Pearson and deviance residuals.
Solution of question 2 of exercise sheet 1.
Lecture 5
3 Abril 2020, 08:00 • Rui Paulo
Video-lecture.
Saturated model, likehood ratio between current and saturated model.
Deviance and scaled deviance. Assessing the statistical significance of nested models. Table of deviances. Examples.
The normal case: the scaled deviance is the residual sum of squares. Relationship with the ANOVA F-tests.
Lecture 4
27 Março 2020, 08:00 • Rui Paulo
Video-lecture.
Generalized linear models: distribution for the data, linear predictor, link function. Examples.
Link function: remarks, canonical link function.
Example: binary response, canonical link function, the SwissLabor dataset. Factor as a regressor. Dummy variables, interpretation of the parameters as increments in the intercept over the reference level. Interaction terms as increments in the slope over the reference level.
Factors with r levels: number of parameters associated. Interaction terms: number of parameters associated.
Lecture 3
20 Março 2020, 08:00 • Rui Paulo
Video-lecture available from TEAMS.
Multiple linear regression: F-tests and diagnostic plots.
Generalized linear models: introduction. The exponential family of distributions: definition, natural parameter, mean and variance. Variance function. Examples.