Depth-based classification of directional data
A non-parametric procedure based on the concept angular depth function is developed for dealing with classification problems of objects in directional statistics. Several notions of depth for directional data are adopted: the angular simplicial, the angular Tukey’s, the arc distance, the cosine dist...
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| Published in | Expert systems with applications Vol. 169; p. 114433 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Elsevier Ltd
01.05.2021
Elsevier BV |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0957-4174 1873-6793 |
| DOI | 10.1016/j.eswa.2020.114433 |
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| Abstract | A non-parametric procedure based on the concept angular depth function is developed for dealing with classification problems of objects in directional statistics. Several notions of depth for directional data are adopted: the angular simplicial, the angular Tukey’s, the arc distance, the cosine distance and the chord distance depths. The proposed method is flexible and can be applied even in high-dimensional cases when a suitable notion of depth is adopted. Performances are investigated and compared by applying methods to different distributional settings through simulated and real data sets.
•A non-parametric spherical-distance-based classifier is proposed.•It is an alternative to the existing depth-based algorithms.•Performances are investigated and compared through simulated and real data sets. |
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| AbstractList | A non-parametric procedure based on the concept angular depth function is developed for dealing with classification problems of objects in directional statistics. Several notions of depth for directional data are adopted: the angular simplicial, the angular Tukey’s, the arc distance, the cosine distance and the chord distance depths. The proposed method is flexible and can be applied even in high-dimensional cases when a suitable notion of depth is adopted. Performances are investigated and compared by applying methods to different distributional settings through simulated and real data sets.
•A non-parametric spherical-distance-based classifier is proposed.•It is an alternative to the existing depth-based algorithms.•Performances are investigated and compared through simulated and real data sets. A non-parametric procedure based on the concept angular depth function is developed for dealing with classification problems of objects in directional statistics. Several notions of depth for directional data are adopted: the angular simplicial, the angular Tukey's, the arc distance, the cosine distance and the chord distance depths. The proposed method is flexible and can be applied even in high-dimensional cases when a suitable notion of depth is adopted. Performances are investigated and compared by applying methods to different distributional settings through simulated and real data sets. |
| ArticleNumber | 114433 |
| Author | D’Ambrosio, Antonio Pandolfo, Giuseppe |
| Author_xml | – sequence: 1 givenname: Giuseppe surname: Pandolfo fullname: Pandolfo, Giuseppe email: giuseppe.pandolfo@unina.it organization: Department of Industrial Engineering, University of Naples Federico II, Italy – sequence: 2 givenname: Antonio surname: D’Ambrosio fullname: D’Ambrosio, Antonio email: antdambr@unina.it organization: Department of Economics and Statistics, University of Naples Federico II, Italy |
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| Cites_doi | 10.1007/s00362-015-0738-3 10.1016/S0167-9473(96)00020-5 10.1111/j.1541-0420.2006.00682.x 10.1007/s11634-010-0066-3 10.1073/pnas.0801715105 10.1080/02331880600766662 10.1111/biom.12889 10.1080/01621459.1954.10501233 10.1198/jasa.2009.0108 10.18637/jss.v051.i04 10.1111/j.1467-9469.2005.00423.x 10.1016/j.jmva.2004.02.013 10.1007/s00362-012-0488-4 10.1002/cjs.11479 10.3150/13-BEJ561 10.1007/s10044-013-0340-z 10.1093/biomet/61.2.335 10.1023/B:CASA.0000012089.39260.b3 10.1093/biomet/69.1.197 10.1214/aos/1018031260 10.1214/14-EJS904 10.1002/bimj.4710390506 10.1214/aos/1176347507 10.1080/01621459.2012.688462 |
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| Keywords | Misclassification rate Spherical random variables Angular data depth Directional distance |
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| Title | Depth-based classification of directional data |
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