Andreas Franz Ruckstuhl: Catalogue data in Spring Semester 2020

Name Prof. Dr. Andreas Franz Ruckstuhl
(Professor ZFH - Zürcher Hochschule für Angewandte Wissenschaften (ZHAW))
E-mailandreasfranz.ruckstuhl@math.ethz.ch
DepartmentMathematics
RelationshipLecturer

NumberTitleECTSHoursLecturers
401-6222-00LRobust and Nonlinear Regression Information Restricted registration - show details 2 credits1V + 1UA. F. Ruckstuhl
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
447-6222-01LRobust Regression Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
1 creditA. F. Ruckstuhl
AbstractThe basic ideas of robust fitting techniques are explained theoretically and practically using regression models and explorative multivariate analysis.
ObjectiveParticipants are familiar with common robust fitting methods for linear regression models as well as for exploratory multivariate analysis and are able to assess their suitability for the data at hand.
ContentInfluence 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.
LiteratureLecture notes are available.
447-6222-02LNonlinear Regression Restricted registration - show details
Only for DAS and CAS in Applied Statistics.
1 creditA. F. Ruckstuhl
AbstractFitting nonlinear regression functions and determining reliable confidence intervals.
ObjectiveParticipants know the challenges that arise in fitting nonlinear regression functions. In addition, they are aware of the difference between classical and profile based methods to determine confidence intervals.
ContentNonlinear regression models, estimation methods, approximate tests and confidence intervals, estimation methods, profile t plot, profile traces, parameter transformations, prediction and calibration.
Lecture notesLecture notes are available.