RT Journal Article
T1 Recent advances in directional statistics
A1 Pewsey, Arthur
A1 García Portugués, Eduardo
AB Mainstream 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.
PB Springer
SN 1133-0686
YR 2021
FD 2021-03-19
LK https://hdl.handle.net/10016/35394
UL https://hdl.handle.net/10016/35394
LA eng
NO This work was supported by GrantsPGC2018-097284-B-100, IJCI-2017-32005 and MTM2016-76969-P from the Spanish Ministry ofEconomy and Competitiveness, and GR18016 from the Junta de Extremadura. All four grants wereco-funded with FEDER funds from the European Union.
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RD 27 jul. 2024