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Coursebooks
Modern regression methods
MATH-408
Lecturer(s) :
Davison Anthony C.Language:
English
Remarque
Course given every two years (given in 2018-19)Summary
A second course on regression modelling, dealing with nonlinear effects of explanatory variables, and non-normal and dependent response variables.Content
Revision of linear regession and likelihood inference
Fitting algorithms for nonlinear models and related diagnostics
Generalised linear model; exponential families; variance and link functions
Proportion and binary responses; logistic regession
Count data and Poisson responses; log-linear models
Overdispersion and quasilikelihood; estimating functions
Mixed models, random effects, generalised additive models and penalized regression
Keywords
Binary response; Count data; Deviance; EM algorithm; Estimating function; Iterative weighted least squares algorithm; Lasso; Likelihood; Logistic regression; Longitudinal data; Mixed model; Multinomial distribution; Overdispersion; Poisson distribution; Quasi-likelihood; Random effects
Learning Prerequisites
Required courses
Knowledge of basic probability and statistics, at, for example, the levels of MATH-240 and MATH-230
Linear models (MATH-341) or equivalent
Important concepts to start the course
Linear regression; likelihood inference; R
Learning Outcomes
By the end of the course, the student must be able to:- Develop theoretical elements needed in regression analysis
- Apply the statistical package R to the analysis of data
- Assess / Evaluate the quality of a model fitted to regression data, and suggest improvements
- Choose a suitable regression model
Transversal skills
- Demonstrate a capacity for creativity.
- Demonstrate the capacity for critical thinking
- Write a scientific or technical report.
Teaching methods
Ex cathedra lectures; homework both theoretical and practical; mini-project
Expected student activities
Attending lectures; solving theoretical problems; solving applied problems using statistical 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
Office hours | Yes |
Assistants | Yes |
Forum | Yes |
Resources
Bibliography
Davison, A. C. (2003) Statistical Models. Cambridge University Press.
Ressources en bibliothèque
In the programs
- Financial engineering, 2018-2019, Master semester 2
- SemesterSpring
- Exam formWritten
- Credits
5 - Subject examined
Modern regression methods - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- Financial engineering, 2018-2019, Master semester 4
- SemesterSpring
- Exam formWritten
- Credits
5 - Subject examined
Modern regression methods - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- Applied Mathematics, 2018-2019, Master semester 2
- SemesterSpring
- Exam formWritten
- Credits
5 - Subject examined
Modern regression methods - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- Applied Mathematics, 2018-2019, Master semester 4
- SemesterSpring
- Exam formWritten
- Credits
5 - Subject examined
Modern regression methods - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- Mathematics - master program, 2018-2019, Master semester 2
- SemesterSpring
- Exam formWritten
- Credits
5 - Subject examined
Modern regression methods - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- Mathematics for teaching, 2018-2019, Master semester 2
- SemesterSpring
- Exam formWritten
- Credits
5 - Subject examined
Modern regression methods - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
- Mathematics for teaching, 2018-2019, Master semester 4
- SemesterSpring
- Exam formWritten
- Credits
5 - Subject examined
Modern regression methods - Lecture
2 Hour(s) per week x 14 weeks - Exercises
2 Hour(s) per week x 14 weeks
- Semester
Reference week
Mo | Tu | We | Th | Fr | |
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8-9 | |||||
9-10 | |||||
10-11 | CM013 | ||||
11-12 | |||||
12-13 | |||||
13-14 | MAA110 | ||||
14-15 | |||||
15-16 | |||||
16-17 | |||||
17-18 | |||||
18-19 | |||||
19-20 | |||||
20-21 | |||||
21-22 |
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- Autumn semester
- Winter sessions
- Spring semester
- Summer sessions
- Lecture in French
- Lecture in English
- Lecture in German