Spatial depth-based classification for functional data

Carlo Sguera (), Pedro Galeano and Rosa Lillo ()

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2014, vol. 23, issue 4, 725-750

Abstract: We enlarge the number of available functional depths by introducing the kernelized functional spatial depth (KFSD). KFSD is a local-oriented and kernel-based version of the recently proposed functional spatial depth (FSD) that may be useful for studying functional samples that require an analysis at a local level. In addition, we consider supervised functional classification problems, focusing on cases in which the differences between groups are not extremely clear-cut or the data may contain outlying curves. We perform classification by means of some available robust methods that involve the use of a given functional depth, including FSD and KFSD, among others. We use the functional k-nearest neighbor classifier as a benchmark procedure. The results of a simulation study indicate that the KFSD-based classification approach leads to good results. Finally, we consider two real classification problems, obtaining results that are consistent with the findings observed with simulated curves. Copyright Sociedad de Estadística e Investigación Operativa 2014

Keywords: Functional depths; Functional outliers; Functional spatial depth; Kernelized functional spatial depth; Supervised functional classification; 62H30; 62H99 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:testjl:v:23:y:2014:i:4:p:725-750

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DOI: 10.1007/s11749-014-0379-1

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