MATH-441 / 5 credits

Teacher:

Language: English

Remark: Cours donné en alternance tous les deux ans (pas donné en 2021-22)

## Summary

In the decades from 1930 to 1950, many rank-based statistics were introduced. These methods were received with much interest, because they worked under weak conditions. Starting in the late 1950, a theory of robustness was added. The course gives an overview of these two approaches to data analysis.

## Required courses

Introduction to Probability, Introduction to Statistics

## Learning Outcomes

By the end of the course, the student must be able to:

• Expound the content of the course.
• Apply the statistical methods explained in the course.
• Sketch the proofs of the theoretical results given in the course.
• Choose the appropriate robust or non parametric methods for a given data analysis problem.
• Differentiate between robust and non-parametric methods.
• Generalize the tools treated in the course to other problems.
• Apply spline and kernel smoothers.
• Apply M-estimatiors in a variety of situations.

## Transversal skills

• Assess one's own level of skill acquisition, and plan their on-going learning goals.
• Manage priorities.

## Teaching methods

Ex cathedra lecture and exercises in the classroom

## Expected student activities

Do all the exercices. Prepare each week for the course. Participate actively in the course.

## Assessment methods

Oral exam.

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.

## Bibliography

Introduction to the theory of nonparametric statistics by R.H. Randles and D.A. Wolfe, Wiley.

All of nonparametric statistics by L. Wasserman, Springer.

Robust Statistics: The approach based on influence functions by F.R. Hampel, E.M. Ronchetti, P.J. Rousseeuw, W.A. Stahel, Wiley.

Robust Statistics by P.J. Huber, Wiley (second edition).

Robust Statistics: Theory and Methods by D.R. Martin, M. Salibian-Barrera, R.A. Maronna, V.J. Yohai, Wiley.

## In the programs

• Semester: Spring
• Exam form: Oral (summer session)
• Subject examined: Robust and nonparametric statistics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Oral (summer session)
• Subject examined: Robust and nonparametric statistics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Oral (summer session)
• Subject examined: Robust and nonparametric statistics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Oral (summer session)
• Subject examined: Robust and nonparametric statistics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks
• Semester: Spring
• Exam form: Oral (summer session)
• Subject examined: Robust and nonparametric statistics
• Lecture: 2 Hour(s) per week x 14 weeks
• Exercises: 2 Hour(s) per week x 14 weeks

## Reference week

 Mo Tu We Th Fr 8-9 9-10 10-11 11-12 12-13 13-14 14-15 15-16 16-17 17-18 18-19 19-20 20-21 21-22