Assessing when a sample is mostly normal

Pedro C. Alvarez-Esteban, Eustasio del Barrio, Juan A. Cuesta-Albertos and Carlos Matrán

Computational Statistics & Data Analysis, 2010, vol. 54, issue 12, 2914-2925

Abstract: 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. The L2-Wasserstein distance is used to 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 the use of data-driven trimming methods instead of the more classical symmetric trimming procedures.

Keywords: Model; assessment; Asymptotics; Impartial; trimming; Wasserstein; distance; Similarity (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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