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dc.contributor.authorMichelot, Theo
dc.contributor.authorGlennie, Richard
dc.contributor.authorHarris, Catriona M
dc.contributor.authorThomas, Len
dc.date.accessioned2021-04-06T16:30:02Z
dc.date.available2021-04-06T16:30:02Z
dc.date.issued2021-03-26
dc.identifier.citationMichelot , T , Glennie , R , Harris , C M & Thomas , L 2021 , ' Varying-coefficient stochastic differential equations with applications in ecology ' , Journal of Agricultural, Biological and Environmental Statistics , vol. First Online . https://doi.org/10.1007/s13253-021-00450-6en
dc.identifier.issn1085-7117
dc.identifier.otherPURE: 273096321
dc.identifier.otherPURE UUID: 11fd9d3a-6edf-418b-afbb-74a491389ede
dc.identifier.otherORCID: /0000-0002-7436-067X/work/92019828
dc.identifier.otherORCID: /0000-0001-9198-2414/work/92019880
dc.identifier.otherORCID: /0000-0003-3806-4280/work/92020094
dc.identifier.otherScopus: 85103355906
dc.identifier.otherWOS: 000633295400001
dc.identifier.urihttps://hdl.handle.net/10023/21779
dc.descriptionThis work was funded by the US office of Naval Research, Grant N000141812807.en
dc.description.abstractStochastic differential equations (SDEs) are popular tools to analyse time series data in many areas, such as mathematical finance, physics, and biology. They provide a mechanistic description of the phenomenon of interest, and their parameters often have a clear interpretation. These advantages come at the cost of requiring a relatively simple model specification. We propose a flexible model for SDEs with time-varying dynamics where the parameters of the process are nonparametric functions of covariates, similar to generalized additive models. Combining the SDE and nonparametric approaches allows for the SDE to capture more detailed, non-stationary, features of the data-generating process. We present a computationally efficient method of approximate inference, where the SDE parameters can vary according to fixed covariate effects, random effects, or basis-penalty smoothing splines. We demonstrate the versatility and utility of this approach with three applications in ecology, where there is often a modelling trade-off between interpretability and flexibility.
dc.format.extent18
dc.language.isoeng
dc.relation.ispartofJournal of Agricultural, Biological and Environmental Statisticsen
dc.rightsCopyright © 2021 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en
dc.subjectContinuous timeen
dc.subjectDiffusion processen
dc.subjectNon-parametricen
dc.subjectSmoothing splinesen
dc.subjectGeneralized additive modelsen
dc.subjectAnimal movementen
dc.subjectQH301 Biologyen
dc.subjectDASen
dc.subject.lccQH301en
dc.titleVarying-coefficient stochastic differential equations with applications in ecologyen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. Statisticsen
dc.contributor.institutionUniversity of St Andrews. School of Biologyen
dc.contributor.institutionUniversity of St Andrews. Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews. Centre for Research into Ecological & Environmental Modellingen
dc.contributor.institutionUniversity of St Andrews. Sea Mammal Research Uniten
dc.contributor.institutionUniversity of St Andrews. Office of the Principalen
dc.contributor.institutionUniversity of St Andrews. Marine Alliance for Science & Technology Scotlanden
dc.identifier.doihttps://doi.org/10.1007/s13253-021-00450-6
dc.description.statusPeer revieweden
dc.identifier.urlhttps://link.springer.com/article/10.1007/s13253-021-00450-6#Sec14en


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