Publication:
Recent advances in directional statistics

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPewsey, Arthur
dc.contributor.authorGarcía Portugués, Eduardo
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2022-07-05T08:00:36Z
dc.date.available2022-07-05T08:00:36Z
dc.date.issued2021-03-19
dc.description.abstractMainstream statistical methodology is generally applicable to data observed inEuclidean space. There are, however, numerous contexts of considerable scientific interest in which the natural supports for the data under consideration are Riemannian manifolds like the unit circle, torus, sphere, and their extensions. Typically, such data can be represented using one or more directions, and directional statistics is the branch of statistics that deals with their analysis. In this paper, we provide a review of the many recent developments in the field since the publication of Mardia and Jupp (Wiley 1999), still the most comprehensive text on directional statistics. Many of those developments have been stimulated by interesting applications in fields as diverse as astronomy, medicine, genetics, neurology, space situational awareness, acoustics, image analysis, text mining, environmetrics, and machine learning. We begin by considering developments for the exploratory analysis of directional data before progressing to distributional models, general approaches to inference, hypothesis testing, regression, nonparametric curve estimation, methods for dimension reduction, classification andclustering, and the modelling of time series, spatial and spatio-temporal data. An overview of currently available software for analysing directional data is also provided, and potential future developments are discussed.en
dc.description.sponsorshipThis work was supported by Grants PGC2018-097284-B-100, IJCI-2017-32005 and MTM2016-76969-P from the Spanish Ministry of Economy and Competitiveness, and GR18016 from the Junta de Extremadura. All four grants were co-funded with FEDER funds from the European Union.en
dc.format.extent58
dc.identifier.bibliographicCitationPewsey, A., & García-Portugués, E. (2021). Recent advances in directional statistics. TEST, 30(1), 1–58.en
dc.identifier.doihttps://doi.org/10.1007/s11749-021-00759-x
dc.identifier.issn1133-0686
dc.identifier.publicationfirstpage1
dc.identifier.publicationissue1
dc.identifier.publicationlastpage58
dc.identifier.publicationtitleTesten
dc.identifier.publicationvolume30
dc.identifier.urihttps://hdl.handle.net/10016/35394
dc.identifier.uxxiAR/0000028042
dc.language.isoeng
dc.publisherSpringeren
dc.relation.projectIDGobierno de España. IJCI-2017-32005es
dc.relation.projectIDGobierno de España. PGC2018-097284-B-100es
dc.relation.projectIDGobierno de España. MTM2016-76969-Pes
dc.rights© Sociedad de Estadística e Investigación Operativa 2021es
dc.rightsAtribución 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherClassificationen
dc.subject.otherClusteringen
dc.subject.otherDimension reductionen
dc.subject.otherDistributional modelsen
dc.subject.otherExploratory data analysisen
dc.subject.otherHypothesis testsen
dc.subject.otherNonparametric methodsen
dc.subject.otherRegressionen
dc.subject.otherSerial dependenceen
dc.subject.otherSoftwareen
dc.subject.otherSpatial statisticsen
dc.titleRecent advances in directional statisticsen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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