dc.contributor.author | Ojo, Oluwasegun | |
dc.contributor.author | Lillo, Rosa Elvira | |
dc.contributor.author | Fernández Anta, Antonio | |
dc.date.accessioned | 2021-07-13T09:49:55Z | |
dc.date.available | 2021-07-13T09:49:55Z | |
dc.date.issued | 2021-05 | |
dc.identifier.uri | http://hdl.handle.net/20.500.12761/980 | |
dc.description.abstract | Outlier detection is one of the standard exploratory analysis tasks in functional data
analysis. We present the R package fdaoutlier which contains implementations of some
of the latest techniques for detecting functional outliers. The package makes it easy to
detect different types of outliers (magnitude, shape, and amplitude) in functional data,
and some of the implemented methods can be applied to both univariate and multivariate
functional data. We illustrate the main functionality of the R package with common
functional datasets in the literature. | |
dc.language.iso | eng | |
dc.publisher | arXiv | |
dc.title | Outlier Detection for Functional Data with R
Package fdaoutlier | en |
dc.type | technical report | |
dc.rights.accessRights | open access | |
dc.identifier.doi | arXiv:2105.05213 [stat.CO] | |
dc.eprint.id | http://eprints.networks.imdea.org/id/eprint/2336 | |