Summer school of the International Association for Statistical Computing (European Regional Section)
Robust methods for advanced data structures
Tuesday 6 September 2011 - Friday 9 September 2011, Leuven (Belgium)
About the IASC-ERS School
The IASC-ERS Summer school is intended to provide training in special areas of statistics for PhD students, junior researchers and lecturers at universities. Professionals working in industry who are interested in the application of new statistical methods are also invited to participate.
Participants are expected to have good background in statistics at the M.Sc. level although not necessarily related to the subject of the course.
Aims of the course
Robust statistical methods remain reliable in presence of model deviation. If outliers are present in the data, the bias of a robust estimator remains bounded. Moreover, the efficiency of a robust estimator should be reasonable high over a large class of model distributions, and not just a single model. The theory and practice of robust statistics is well developed for location, scale, and regression problems. Over the last decade, significant progress has been made in the development of robust methods for more advanced data structures. In this summer school we do not only introduce the basic ideas and principles of robustness, but we also cover recently proposed methods for more complex statistical models.
The course is given at the research level, but does not require preliminary knowledge of robust statistics.
Both theoretical and implementation aspects are covered. There is also opportunity for junior participants to present their first research results.
Organizing committee
Following speakers are invited:
- Stefan Van Aelst (Ghent University, Belgium)
- Eva Cantoni (University of Genève, Switzerland)
- Hannu Oja (University of Tampere, Finland)
- Peter Rousseeuw (Katholieke Universiteit Leuven)
- Christophe Croux (Katholieke Universiteit Leuven)
- Mia Hubert (Katholieke Universiteit Leuven)
Sponsors:
Arenberg Doctoral School of Science, Engineering & Technology
