Regression methods
Summary
General graduate course on regression methods
Content
Linear regression and analysis of variance. Geometric interpretation. Properties of estimators. Orthogonality and balance. Diagnostics. Transformations. Variable selection and post-selection inference. Robustness and estimating equations. Quantile regression.
PIWLS algorithm and general regression models. Generalized linear models; logistic regession; count data and Poisson responses.
Penalised regression: ridge, lasso, elastic net, thresholding. Control of statistical errors.
Components of variance: nested and crossed effects, mixed models. REML.
Spline smoothing, estimation and inference. Additive models. Generalised additive models. Nonparametric regression.
Keywords
Binary response. Count data. Deviance. Least squares. Likelihood. Mixed model. Penalised regression model. Random effects. Ridge regression.
Learning Outcomes
By the end of the course, the student must be able to:
- Develop elements needed in a regression analysis
- Apply the statistical package R for the analysis of data
- Assess / Evaluate the quality of a model
- Formulate a suitable regression model and assess its validity
Transversal skills
- Demonstrate the capacity for critical thinking
- Demonstrate a capacity for creativity.
Teaching methods
Ex cathedra lectures.
Expected student activities
Attending lectures; solving theoretical problems; solving applied problems using suitable software
Assessment methods
Written midterm and final exams.
Dans le cas de l'art. 3 al. 5 du Règlement de section, l'enseignant décide de la forme de l'examen qu'il communique aux étudiants concernés.
Supervision
| Office hours | No |
| Assistant.e.s | Yes |
Resources
Virtual desktop infrastructure (VDI)
No
Bibliography
Davison, A. C. (2003) Statistical Models.
Ressources en bibliothèque
Notes/Handbook
Slides and exercise sheets with corrections weekly published on moodle. See Moodle page.
Moodle Link
Dans les plans d'études
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: obligatoire
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: obligatoire
- Semestre: Automne
- Forme de l'examen: Ecrit (session d'hiver)
- Matière examinée: Regression methods
- Cours: 2 Heure(s) hebdo x 14 semaines
- Exercices: 2 Heure(s) hebdo x 14 semaines
- Type: optionnel