B1064
Title: Robustness against cellwise outliers
Authors: Peter Rousseeuw - KU Leuven (Belgium) [presenting]
Abstract: A multivariate dataset consists of $n$ observations in $p$ dimensions, and is often stored in an $n$ by $p$ matrix $X$. Robust statistics has mostly focused on identifying and downweighting outlying rows of $X$, called rowwise or casewise outliers. However, downweighting an entire row if only one (or a few) of its cells are deviating entails a huge loss of information. Also, in high-dimensional data the majority of the rows may contain a few contaminated cells, which yields a loss of robustness as well. Recently new robust methods have been developed for datasets with missing values and with cellwise outliers, also called elementwise outliers. Several methods of this type will be studied and compared in terms of their robustness as well as their statistical and computational efficiency. Simulation results will be shown as well as real data examples.
