| Autor: |
Sguera, Carlo, Galeano, Pedro, Lillo, Rosa |
| Rok vydání: |
2013 |
| Předmět: |
|
| Zdroj: |
TEST, December 2014, Volume 23, Issue 4, pp 725-750 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1007/s11749-014-0379-1 |
| Popis: |
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 \textit{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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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