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

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Classification and Data Mining

Abstract

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.

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Author information

Authors and Affiliations

  1. Dipartimento di Studi Europei e Mediterranei, Seconda Università degli Studi di Napoli, Napoli, Italy

    Elvira Romano

  2. Departamento de Matematicas, Universitat Jaume I, Castellon de la Plana, Spain

    Jorge Mateu

Authors
  1. Elvira Romano
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  2. Jorge Mateu
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Corresponding author

Correspondence to Elvira Romano .

Editor information

Editors and Affiliations

  1. Department of Statistics, Università degli Studi di Firenze, Viale G.B. Morgagni 59, Firenze, 50134, Italy

    Antonio Giusti

  2. Fakultät für Informatik, und Mathematik, Universität Passau, Innstr. 33, Passau, 94030, Germany

    Gunter Ritter

  3. Sapienza", Department of Statistics, University of Rome "La, Piazzale Aldo Moro 5, Rome, 00185, Italy

    Maurizio Vichi

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Romano, E., Mateu, J. (2013). Outlier Detection for Geostatistical Functional Data: An Application to Sensor Data. In: Giusti, A., Ritter, G., Vichi, M. (eds) Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28894-4_16

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