## Special issue on variable selection and robust procedures

- Stefan Van Aelst, Roy Welsch, Ruben H. Zamar
- Computational Statistics & Data Analysis
- 2010

1 Excerpt

- Published 2010 in Computational Statistics & Data Analysis

The use of trimming procedures constitutes a natural approach to robustifying statistical methods. This is the case of goodness-of-fit tests based on a distance, which can be modified by choosing trimmed versions of the distributions minimizing that distance. In this paper we consider the L2-Wasserstein distance and introduce the trimming methodology for assessing when a data sample can be considered mostly normal. The method can be extended to other location and scale models, introducing a robust approach to model validation, and allows an additional descriptive analysis by determining the subset of the data with the best improved fit to the model. This is a consequence of our use of data-driven trimming methods instead of more classical symmetric trimming procedures.

@article{lvarezEsteban2010AssessingWA,
title={Assessing when a sample is mostly normal},
author={Pedro C. {\'A}lvarez-Esteban and Eustasio del Barrio and Juan Antonio Cuesta-Albertos and Carlos Matr{\'a}n},
journal={Computational Statistics & Data Analysis},
year={2010},
volume={54},
pages={2914-2925}
}