Statistical Modelling

About

This course is the first part of the L3 statistics course, the second part being devoted to tests and model choice, taught by Vincent Rivoirard. This first part covers fundamentals of parametric statistics, both from mathematical and methodological points of view. With some forays into computational statistics like the bootstrap and Monte Carlo methods. The main theme is that modelling is an inherent part of the statistical practice, rather than an antecedent to the statistical step. Data may be a given, while models almost never are. This means one should keep a critical eye about models and develop critical tools to assess their adequation. Including, first and foremost, an assessment by simulation (Monte Carlo) methods.

As you may have noticed, the course is entirely in English, except for the partial and final exams. This means the practicals (TD) are in English as well and students are expected to communicate in English. There is indeed at least one course taught in English in each semester of the L3 and M1 years: Statistical Modelling is the chosen course for the first semester of L3.

Some practicals (TP) will be included, covering R language programming and applications to the bootstrap and Monte Carlo methodologies.

References

Wasserman, L. (2010) All of Statistics: A concise course in statistical inference. Springer-Verlag [Springer link, with discount code tCmCBtT43NBdmWr] [amazon link]
Boos, D. and Stefanski, L.A. (2014) Essential statistical inference. Springer-Verlag
Cadre, B. et Vial. C. (2012) Statistique mathématique - cours et exercices corrigés. Ellipses [en français]
Fourdrinier, D. (2002) Statistique inférentielle : cours et exercices corrigés. Sciences Sup [en français]

Course Material

Instructor: Christian Robert

Slides of the course [en anglais] :
- Chapter 0 | Chapter 1 | Chapter 2 | Chapter 3 | Chapter 4
Videos [on Teams]

Tutorials

Instructors: Charly Andral, Nassim Bouarach, Emma Kopp

About Probabilities: TD n°1 | TD n°2
Statistical models: TD n°3
Information theory and inference: TD n°4 | TD n°5

Practical