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dc.contributor.authorMiller, David L.
dc.contributor.authorGlennie, Richard
dc.contributor.authorSeaton, Andrew E.
dc.date.accessioned2019-09-23T12:30:05Z
dc.date.available2019-09-23T12:30:05Z
dc.date.issued2020-03-01
dc.identifier.citationMiller , D L , Glennie , R & Seaton , A E 2020 , ' Understanding the stochastic partial differential equation approach to smoothing ' , Journal of Agricultural, Biological and Environmental Statistics , vol. 25 , no. 1 , pp. 1-16 . https://doi.org/10.1007/s13253-019-00377-zen
dc.identifier.issn1085-7117
dc.identifier.otherPURE: 261009529
dc.identifier.otherPURE UUID: 6c694f09-a063-437f-ac14-6c138d20a86e
dc.identifier.otherORCID: /0000-0003-3806-4280/work/62311842
dc.identifier.otherScopus: 85073817391
dc.identifier.otherWOS: 000511790200001
dc.identifier.urihttp://hdl.handle.net/10023/18538
dc.descriptionDLM was funded by OPNAV N45 and the SURTASS LFA Settlement Agreement, being managed by the U.S. Navy's Living Marine Resources program under Contract No. N39430-17-C-1982.en
dc.description.abstractCorrelation and smoothness are terms used to describe a wide variety of random quantities. In time, space, and many other domains, they both imply the same idea: quantities that occur closer together are more similar than those further apart. Two popular statistical models that represent this idea are basis-penalty smoothers (Wood in Texts in statistical science, CRC Press, Boca Raton, 2017) and stochastic partial differential equations (SPDEs) (Lindgren et al. in J R Stat Soc Series B (Stat Methodol) 73(4):423–498, 2011). In this paper, we discuss how the SPDE can be interpreted as a smoothing penalty and can be fitted using the R package mgcv, allowing practitioners with existing knowledge of smoothing penalties to better understand the implementation and theory behind the SPDE approach.
dc.format.extent16
dc.language.isoeng
dc.relation.ispartofJournal of Agricultural, Biological and Environmental Statisticsen
dc.rightsCopyright © The Author(s) 2019. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectSmoothingen
dc.subjectStochastic partial differential equationsen
dc.subjectGeneralized additive modelen
dc.subjectSpatial modellingen
dc.subjectBasis-penalty smoothingen
dc.subjectQA Mathematicsen
dc.subjectDASen
dc.subjectBDCen
dc.subject.lccQAen
dc.titleUnderstanding the stochastic partial differential equation approach to smoothingen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews. Applied Mathematicsen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.identifier.doihttps://doi.org/10.1007/s13253-019-00377-z
dc.description.statusPeer revieweden
dc.identifier.urlhttps://link.springer.com/article/10.1007%2Fs13253-019-00377-z#Sec16en


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