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dc.creatorCarrizosa Priego, Emilio Josées
dc.creatorOlivares Nadal, Alba Victoriaes
dc.creatorRamírez Cobo, Josefaes
dc.date.accessioned2018-01-03T08:28:18Z
dc.date.available2018-01-03T08:28:18Z
dc.date.issued2017-04
dc.identifier.issn1465-4644es
dc.identifier.issn1468-4357es
dc.identifier.urihttp://hdl.handle.net/11441/68116
dc.description.abstractVector autoregressive (VAR) models constitute a powerful and well studied tool to analyze multivariate time series. Since sparseness, crucial to identify and visualize joint dependencies and relevant causalities, is not expected to happen in the standard VAR model, several sparse variants have been introduced in the literature. However, in some cases it might be of interest to control some dimensions of the sparsity, as e.g. the number of causal features allowed in the prediction. To authors extent none of the existent methods endows the user with full control over the different aspects of the sparsity of the solution. In this paper we propose a sparsity-controlled VAR model which allows to control different dimensions of the sparsity, enabling a proper visualization of potential causalities and dependencies. The model coefficients are found as the solution to a mathematical optimization problem, solvable by standard numerical optimization routines. The tests performed on both simulated and real-life multivariate time series show that our approach may outperform both the standard and Group Lasso in terms of prediction errors specially when highly sparse graphs are sought, while avoiding the VAR’s overfitting for more dense graphs. Causality; Mixed Integer Non Linear Programming; multivariate time series; sparse models; Vector autoregressive process.es
dc.description.sponsorshipMinisterio de Econom´ıa y Competitividades
dc.description.sponsorshipJunta de Andalucíaes
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherOxford University Presses
dc.relation.ispartofBiostatistics, 18 (2), 244-259.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectVector autoregressive modelses
dc.titleA sparsity-controlled vector autoregressive modeles
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Estadística e Investigación Operativaes
dc.relation.projectIDMTM2012-36163es
dc.relation.projectIDP11-FQM-7603es
dc.relation.projectIDFQM-329es
dc.relation.publisherversionhttps://academic.oup.com/biostatistics/article-pdf/18/2/244/11057433/kxw042.pdfes
dc.identifier.doi10.1093/biostatistics/kxw042es
dc.contributor.groupUniversidad de Sevilla. FQM329: Optimizaciónes
idus.format.extent27 p.es
dc.journaltitleBiostatisticses
dc.publication.volumen18es
dc.publication.issue2es
dc.publication.initialPage244es
dc.publication.endPage259es
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). España
dc.contributor.funderJunta de Andalucía

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional