Software Tools for Robust Analysis of High-Dimensional Data

Abstract

The present work discusses robust multivariate methods specifically designed for high dimensions. Their implementation in R is presented and their application is illustrated on examples. The first group are algorithms for outlier detection, already introduced elsewhere and implemented in other packages. The value added of the new package is that all methods follow the same design pattern and thus can use the same graphical and diagnostic tools. The next topic covered is sparse principal components including an object oriented interface to the standard method proposed by Zou, Hastie, and Tibshirani (2006) and the robust one proposed by Croux, Filzmoser, and Fritz (2013). Robust partial least squares (see Hubert and Vanden Branden 2003) as well as partial least squares for discriminant analysis conclude the scope of the new package.