Robust Statistics
The aim of this course is to acquire knowledge and insight in robust statistical methods, and to be able to apply those methods to real data.
Praktische info:
Inschrijven?
- Voorwaarden: familiarity with basic multivariate statistical methods (location and covariance estimation, multiple regression, principal component analysis) & familiarity with the R software or Matlab.
- Prijs: €275-€1500
georganiseerd door:
Leertraject
Robust statistical methods are resistant to outlying observations in the data, and hence are also able to detect these outliers. In this course we will introduce modern robust statistical methods for univariate and multivariate data.
We study several robust estimators of location, scale, skewness, correlation, covariance and regression. For the analysis of high-dimensional data we discuss robust estimators for principal component analysis, principal component regression and partial least squares regression. Finally we consider robust methods for classification. Also notions of robustness such as breakdown point and influence function will be introduced.
During the morning sessions we study the methods and their robustness properties. We also discuss computational issues. The afternoon sessions are devoted to the analysis of real data sets using Matlab or R software.
Dates
From 10:30 to 12:30
- 12, 19 and 26 February 2025
- 5, 12, 19 and 26 March 2025
- 2, 23 and 30 April 2025
- 7, 14, 21 May 2025
From 14:00 to 16:00
- 6 and 20 March 2025
- 24 April 2025
- 8 May 2025
Lesgever/spreker
Peter Rousseeuw
Peter Rousseeuw is professor in statistics at Katholieke Universiteit Leuven. He obtained his Ph.D. on the topic of robust statistics in 1981, and has been a full professor in universities in the Netherlands, Switzerland, and Belgium. He has (co-)authored over 160 papers and three Wiley-Interscience books.
In 2003 ISI-Thompson included him in their list of Highly Cited Mathematicians. He is an elected member of the International Statistical Institute and a fellow of the Institute of Mathematical Statistics and the American Statistical Association.
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