401-6222-00L  Robust and Nonlinear Regression

SemesterSpring Semester 2020
LecturersA. F. Ruckstuhl
Periodicitytwo-yearly recurring course
Language of instructionEnglish


401-6222-00 VRobust and Nonlinear Regression Special students and auditors need a special permission from the lecturers.
Block course
12s hrs
08.06.08:15-10:00HG D 1.2 »
13:15-15:00HG D 1.2 »
15.06.08:15-10:00HG D 1.2 »
13:15-15:00HG D 1.2 »
22.06.08:15-10:00HG D 1.2 »
13:15-15:00HG D 1.2 »
A. F. Ruckstuhl
401-6222-00 URobust and Nonlinear Regression Special students and auditors need a special permission from the lecturers.
Block course
9s hrs
08.06.10:15-12:00HG D 1.2 »
15:15-17:00HG D 1.2 »
15.06.10:15-12:00HG D 1.2 »
15:15-17:00HG D 1.2 »
22.06.10:15-12:00HG D 1.2 »
15:15-17:00HG D 1.2 »
A. F. Ruckstuhl

Catalogue data

AbstractIn a first part, the basic ideas of robust fitting techniques are explained theoretically and practically using regression models and explorative multivariate analysis.

The second part addresses the challenges of fitting nonlinear regression functions and finding reliable confidence intervals.
ObjectiveParticipants are familiar with common robust fitting methods for the linear regression models as well as for exploratory multivariate analysis and are able to assess their suitability for the data at hand.

They know the challenges that arise in fitting of nonlinear regression functions, and know the difference between classical and profile based methods to determine confidence intervals.

They can apply the discussed methods in practise by using the statistics software R.
ContentRobust fitting: influence function, breakdown point, regression M-estimation, regression MM-estimation, robust inference, covariance estimation with high breakdown point, application in principal component analysis and linear discriminant analysis.

Nonlinear regression: the nonlinear regression model, estimation methods, approximate tests and confidence intervals, estimation methods, profile t plot, profile traces, parameter transformation, prediction and calibration
Lecture notesLecture notes are available
Prerequisites / NoticeIt is a block course on three Mondays in June

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits2 credits
ExaminersA. F. Ruckstuhl
Typeungraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

Main linkNonlinear and Robust Regression
Only public learning materials are listed.


No information on groups available.


General : Special students and auditors need a special permission from the lecturers

Offered in

Statistics MasterStatistical and Mathematical CoursesWInformation