Outlier Detection for Geostatistical Functional Data: An Application to Sensor Data

  • Elvira Romano
  • Jorge Mateu
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


In this paper we propose an outlier detection method for geostatistical functional data. Our approach generalizes the functional proposal of Febrero et al. (Comput 5 Stat 22(3):411–427, 2007; Environmetrics 19(4):331–345, 2008) in the spatial framework. It is based on the concept of the kernelized functional modal depth that we have opportunely defined extending the functional modal depth. As an illustration, the methodology is applied to sensor data corresponding to long-term daily climatic time series from meteorological stations.


Depth Function Functional Data Outlier Detection Functional Data Analysis Functional Context 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dipartimento di Studi Europei e MediterraneiSeconda Università degli Studi di NapoliNapoliItaly
  2. 2.Departamento de MatematicasUniversitat Jaume ICastellon de la PlanaSpain

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