A0205
Title: Robust high-dimensional data analysis
Authors: Stefan Van Aelst - University of Leuven (Belgium) [presenting]
Tim Verdonck - KU Leuven and UAntwerpen - imec (Belgium)
Abstract: Robust statistics develops methods and techniques to reliably analyze data in the presence of outlying measurements. Next to robust inference outlier detection is also an important goal of robust statistics. When analyzing high-dimensional data sparse solutions are often desired to enhance interpretability of the results. Moreover, when the data are of uneven quality robust estimators are needed that are computationally efficient such that solutions can be obtained in a reasonable amount of time. Moreover, if many variables in high-dimensional data can have some anomalies in their measurements, then it is not reasonable anymore to assume that a majority of the cases is completely free of contamination. In such cases the standard paradigm of robust statistics is not valid anymore, but alternative methods need to be used. Robust procedures for high-dimensional data, such as estimation of location and scatter, linear regression, generalized linear models and principal component analysis. The good performance of these methods is illustrated on real data using R.
