TY - JOUR AU - Todorov, Valentin AU - Filzmoser, Peter PY - 2014/06/13 Y2 - 2024/03/29 TI - Software Tools for Robust Analysis of High-Dimensional Data JF - Austrian Journal of Statistics JA - AJS VL - 43 IS - 4 SE - Articles DO - 10.17713/ajs.v43i4.44 UR - https://www.ajs.or.at/index.php/ajs/article/view/vol43-4-4 SP - 255-266 AB - <p>The present work discusses robust multivariate methods specifically designed for high<br />dimensions. Their implementation in R is presented and their application is illustrated<br />on examples. The first group are algorithms for outlier detection, already introduced<br />elsewhere and implemented in other packages. The value added of the new package is<br />that all methods follow the same design pattern and thus can use the same graphical<br />and diagnostic tools. The next topic covered is sparse principal components including an<br />object oriented interface to the standard method proposed by Zou, Hastie, and Tibshirani<br />(2006) and the robust one proposed by Croux, Filzmoser, and Fritz (2013). Robust partial<br />least squares (see Hubert and Vanden Branden 2003) as well as partial least squares for<br />discriminant analysis conclude the scope of the new package.</p> ER -