Functional outlier detection with a local spatial depth

Carlo Sguera, Pedro Galeano and Rosa Elvira Lillo Rodríguez

DES - Working Papers. Statistics and Econometrics. WS from Universidad Carlos III de Madrid. Departamento de Estadística

Abstract: This paper proposes methods to detect outliers in functional datasets. We are interested in challenging scenarios where functional samples are contaminated by outliers that may be difficult to recognize. The task of identifying a typical curves is carried out using the recently proposed kernelized functional spatial depth (KFSD). KFSD is a localdepth that can be used to order the curves of a sample from the most to the least central. Since outliers are usually among the least central curves, we introduce three new procedures that provide a threshold value for KFSD such that curves with depth values lower than the threshold are detected as outliers. The results of a simulation study show that our proposals generally out perform a battery of competitors. Finally, we consider areal application with environmental data consisting in levels of nitrogen oxides

Keywords: Functional; depths; Functional; outlier; detection; Kernelized; functional; spatial; depth; Nitrogen; oxides; Smoothed; resampling (search for similar items in EconPapers)
Date: 2014-06
New Economics Papers: this item is included in nep-ecm and nep-ure
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:cte:wsrepe:ws141410

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