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dc.contributor.authorHelske, Jouni
dc.date.accessioned2022-04-06T07:17:01Z
dc.date.available2022-04-06T07:17:01Z
dc.date.issued2022
dc.identifier.citationHelske, J. (2022). Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R. <i>SoftwareX</i>, <i>18</i>, Article 101016. <a href="https://doi.org/10.1016/j.softx.2022.101016" target="_blank">https://doi.org/10.1016/j.softx.2022.101016</a>
dc.identifier.otherCONVID_117615542
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/80507
dc.description.abstractThe R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as changes in tax policy which gradually increases their effect over time. The Markov chain Monte Carlo algorithms powering the Bayesian inference are based on Hamiltonian Monte Carlo provided by Stan software, using a state space representation of the model to marginalize over the regression coefficients for efficient low-dimensional sampling.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier BV
dc.relation.ispartofseriesSoftwareX
dc.rightsCC BY 4.0
dc.subject.otherBayesian inference
dc.subject.othertime-varying regression
dc.subject.otherR
dc.subject.otherMarkov chain Monte Carlo
dc.titleEfficient Bayesian generalized linear models with time-varying coefficients : The walker package in R
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202204062188
dc.contributor.laitosMatematiikan ja tilastotieteen laitosfi
dc.contributor.laitosDepartment of Mathematics and Statisticsen
dc.contributor.oppiaineTilastotiedefi
dc.contributor.oppiaineStatisticsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2352-7110
dc.relation.volume18
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 The Author(s). Published by Elsevier B.V.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber312605
dc.relation.grantnumber311877
dc.relation.grantnumber284513
dc.relation.grantnumber331817
dc.subject.ysobayesilainen menetelmä
dc.subject.ysoMonte Carlo -menetelmät
dc.subject.ysoMarkovin ketjut
dc.subject.ysolineaariset mallit
dc.subject.ysoregressioanalyysi
dc.subject.ysoR-kieli
dc.subject.ysoaikasarjat
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p6361
jyx.subject.urihttp://www.yso.fi/onto/yso/p13075
jyx.subject.urihttp://www.yso.fi/onto/yso/p25748
jyx.subject.urihttp://www.yso.fi/onto/yso/p2130
jyx.subject.urihttp://www.yso.fi/onto/yso/p24355
jyx.subject.urihttp://www.yso.fi/onto/yso/p12290
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1016/j.softx.2022.101016
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderResearch Council of Finlanden
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
dc.relation.funderSuomen Akatemiafi
jyx.fundingprogramResearch costs of Academy Research Fellow, AoFen
jyx.fundingprogramResearch profiles, AoFen
jyx.fundingprogramResearch costs of Academy Research Fellow, AoFen
jyx.fundingprogramAcademy Project, AoFen
jyx.fundingprogramAkatemiatutkijan tutkimuskulut, SAfi
jyx.fundingprogramProfilointi, SAfi
jyx.fundingprogramAkatemiatutkijan tutkimuskulut, SAfi
jyx.fundingprogramAkatemiahanke, SAfi
jyx.fundinginformationThis work has been supported by the Academy of Finland research grants 284513, 312605, 311877, and 331817.
dc.type.okmA1


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