Regression methods

Summary

General graduate course on regression methods

Content

Keywords

Binary response. Count data.  Deviance. Least squares. Likelihood.  Mixed model. Overdispersion. Penalised regression model. Random effects.  Ridge regression.

Learning Prerequisites

Required courses

Courses on basic probability and statistics (e.g., MATH-240, MATH-230) and a first course on the linear model (e.g., MATH-341).

Important concepts to start the course

Linear regession.  Likelihood inference.  Use of computer package R.

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.
• Write a scientific or technical report.

Teaching methods

Ex cathedra lectures; homework both theoretical and applied; mini-project

Expected student activities

Attending lectures; solving theoretical problems; solving applied problems using suitable software

Assessment methods

Written final exam.  Mini-project.

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

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

Davison, A. C. (2003) Statistical Models.

Ressources en bibliothèque

• Statistical Models / Davison

Notes/Handbook

See moodle page

Moodle Link

• https://go.epfl.ch/MATH-408