In 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.
Learning objective
Participants 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.
Content
Robust 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 notes
Lecture notes are available
Prerequisites / Notice
It 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
: Special students and auditors need a special permission from the lecturers