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Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels
10.1002/env.878.abs
This paper analyzes outlier detection for functional data by means of functional depths, which measures the centrality of a given curve within a group of trajectories providing center‐outward orderings of the set of curves. We give some insights of the usefulness of looking for outliers in functional datasets and propose a method based in depths for the functional outlier detection. The performance of the proposed procedure is analyzed by several Monte Carlo experiments. Finally, we illustrate the procedure by finding outliers in a dataset of NOx (nitrogen oxides) emissions taken from a control station near an industrial area. Copyright © 2007 John Wiley & Sons, Ltd.
Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels
10.1002/env.878.abs
This paper analyzes outlier detection for functional data by means of functional depths, which measures the centrality of a given curve within a group of trajectories providing center‐outward orderings of the set of curves. We give some insights of the usefulness of looking for outliers in functional datasets and propose a method based in depths for the functional outlier detection. The performance of the proposed procedure is analyzed by several Monte Carlo experiments. Finally, we illustrate the procedure by finding outliers in a dataset of NOx (nitrogen oxides) emissions taken from a control station near an industrial area. Copyright © 2007 John Wiley & Sons, Ltd.
Outlier detection in functional data by depth measures, with application to identify abnormal NOx levels
Febrero, Manuel (Autor:in) / Galeano, Pedro (Autor:in) / González‐Manteiga, Wenceslao (Autor:in)
Environmetrics ; 19 ; 331-345
01.06.2008
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Outlier Detection in Multivariate Hydrologic Data
Online Contents | 2008
|Outlier Detection in Multivariate Hydrologic Data
British Library Online Contents | 2008
|