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dc.contributor.authorAltieri, L.
dc.contributor.authorCocchi, D.
dc.contributor.authorGreco, F.
dc.contributor.authorIllian, J. B.
dc.contributor.authorScott, E. M.
dc.date.accessioned2017-02-19T00:32:41Z
dc.date.available2017-02-19T00:32:41Z
dc.date.issued2016
dc.identifier.citationAltieri , L , Cocchi , D , Greco , F , Illian , J B & Scott , E M 2016 , ' Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes ' , Journal of Statistical Computation and Simulation , vol. 86 , no. 13 , pp. 2531-2545 . https://doi.org/10.1080/00949655.2016.1146280en
dc.identifier.issn0094-9655
dc.identifier.otherPURE: 241426013
dc.identifier.otherPURE UUID: 08b33611-ca70-44e6-a4b0-bc2cef262605
dc.identifier.otherScopus: 84958755667
dc.identifier.otherWOS: 000378717400004
dc.identifier.urihttp://hdl.handle.net/10023/10323
dc.descriptionAs regards authors Linda Altieri and Fedele Greco, the research work underlying this paper was partially funded by an FIRB 2012 [grant number RBFR12URQJ]; title: Statistical modelling of environmental phenomena: pollution, meteorology, health and their interactions) for research projects by the Italian Ministry of Education, Universities and Research.en
dc.description.abstractThis work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.
dc.format.extent15
dc.language.isoeng
dc.relation.ispartofJournal of Statistical Computation and Simulationen
dc.rights© 2016 Informa UK Limited, trading as Taylor & Francis Group. This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://dx.doi.org/10.1080/00949655.2016.1146280en
dc.subjectEarthquake dataen
dc.subjectChangepoint analysisen
dc.subjectSpatio-temporal point processesen
dc.subjectSpatial effecten
dc.subjectLog-Gaussian Cox processesen
dc.subjectBayesian P-splinesen
dc.subjectParallel computingen
dc.subject62H11en
dc.subject62M30en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQA Mathematicsen
dc.subjectApplied Mathematicsen
dc.subjectStatistics and Probabilityen
dc.subjectModelling and Simulationen
dc.subjectStatistics, Probability and Uncertaintyen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccQAen
dc.titleBayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processesen
dc.typeJournal articleen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Mathematics and Statisticsen
dc.contributor.institutionUniversity of St Andrews.Scottish Oceans Instituteen
dc.contributor.institutionUniversity of St Andrews.Centre for Research into Ecological & Environmental Modellingen
dc.identifier.doihttps://doi.org/10.1080/00949655.2016.1146280
dc.description.statusPeer revieweden
dc.date.embargoedUntil2017-02-18


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