Coursebooks

Robust and nonparametric statistics

MATH-441

Lecturer(s) :

Morgenthaler Stephan

Language:

English

Remarque

Cours donné en alternance sur deux ans (donné en 2019-20)

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.

Content

I. Robust Statistics

II. Linear Rank Tests

III. Estimation of smooth functions

Learning Prerequisites

Required courses

Introduction to Probability, Introduction to Statistics

Learning Outcomes

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

Transversal skills

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.

Resources

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.

Ressources en bibliothèque

In the programs

  • Mathematics - master program, 2019-2020, Master semester 2
    • Semester
      Spring
    • Exam form
      Oral
    • Credits
      5
    • Subject examined
      Robust and nonparametric statistics
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Applied Mathematics, 2019-2020, Master semester 2
    • Semester
      Spring
    • Exam form
      Oral
    • Credits
      5
    • Subject examined
      Robust and nonparametric statistics
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Applied Mathematics, 2019-2020, Master semester 4
    • Semester
      Spring
    • Exam form
      Oral
    • Credits
      5
    • Subject examined
      Robust and nonparametric statistics
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Data Science, 2019-2020, Master semester 2
    • Semester
      Spring
    • Exam form
      Oral
    • Credits
      5
    • Subject examined
      Robust and nonparametric statistics
    • Lecture
      2 Hour(s) per week x 14 weeks
    • Exercises
      2 Hour(s) per week x 14 weeks
  • Data Science, 2019-2020, Master semester 4
    • Semester
      Spring
    • Exam form
      Oral
    • Credits
      5
    • 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

MoTuWeThFr
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
Under construction
Lecture
Exercise, TP
Project, other

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  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German