Andreas Franz Ruckstuhl: Katalogdaten im Frühjahrssemester 2020 |
| Name | Herr Prof. Dr. Andreas Franz Ruckstuhl (Professor ZFH - Zürcher Hochschule für Angewandte Wissenschaften (ZHAW)) |
| andreasfranz.ruckstuhl@math.ethz.ch | |
| Departement | Mathematik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 401-6222-00L | Robust and Nonlinear Regression   | 2 KP | 1V + 1U | A. F. Ruckstuhl | |
| Kurzbeschreibung | 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. | ||||
| Lernziel | 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. | ||||
| Inhalt | 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 | ||||
| Skript | Lecture notes are available | ||||
| Voraussetzungen / Besonderes | It is a block course on three Mondays in June | ||||
| 447-6222-01L | Robust Regression Nur für DAS und CAS in Angewandter Statistik. | 1 KP | A. F. Ruckstuhl | ||
| Kurzbeschreibung | The basic ideas of robust fitting techniques are explained theoretically and practically using regression models and explorative multivariate analysis. | ||||
| Lernziel | Participants 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. | ||||
| Inhalt | 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. | ||||
| Literatur | Lecture notes are available. | ||||
| 447-6222-02L | Nonlinear Regression Nur für DAS und CAS in Angewandter Statistik. | 1 KP | A. F. Ruckstuhl | ||
| Kurzbeschreibung | Fitting nonlinear regression functions and determining reliable confidence intervals. | ||||
| Lernziel | Participants 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. | ||||
| Inhalt | Nonlinear regression models, estimation methods, approximate tests and confidence intervals, estimation methods, profile t plot, profile traces, parameter transformations, prediction and calibration. | ||||
| Skript | Lecture notes are available. | ||||