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dc.contributor.authorKuronen, Mikko
dc.contributor.authorSärkkä, Aila
dc.contributor.authorVihola, Matti
dc.contributor.authorMyllymäki, Mari
dc.date.accessioned2021-08-23T11:48:25Z
dc.date.available2021-08-23T11:48:25Z
dc.date.issued2022
dc.identifier.citationKuronen, M., Särkkä, A., Vihola, M., & Myllymäki, M. (2022). Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests. <i>Environmental and Ecological Statistics</i>, <i>29</i>(1), 185-205. <a href="https://doi.org/10.1007/s10651-021-00514-3" target="_blank">https://doi.org/10.1007/s10651-021-00514-3</a>
dc.identifier.otherCONVID_100248208
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/77454
dc.description.abstractWe propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points xx affects another set of points yy but not vice versa. We use the model to investigate the effect of large trees on the locations of seedlings. In the model, every point in xx has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The parameters of the model are estimated in a Bayesian framework using Markov chain Monte Carlo where a Laplace approximation is used for the Gaussian field of the LGCP model. The proposed model is used to analyze the effect of large trees on the success of regeneration in uneven-aged forest stands in Finland.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer Science and Business Media LLC
dc.relation.ispartofseriesEnvironmental and Ecological Statistics
dc.rightsCC BY 4.0
dc.subject.otherBayesian inference
dc.subject.othercompetition kernel
dc.subject.otherLaplace approximation
dc.subject.otherMCMC
dc.subject.otherspatial random effects
dc.subject.othertree regeneration
dc.titleHierarchical log Gaussian Cox process for regeneration in uneven-aged forests
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202108234618
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.format.pagerange185-205
dc.relation.issn1352-8505
dc.relation.numberinseries1
dc.relation.volume29
dc.type.versionpublishedVersion
dc.rights.copyright© 2021 the Authors
dc.rights.accesslevelopenAccessfi
dc.subject.ysoMarkovin ketjut
dc.subject.ysoMonte Carlo -menetelmät
dc.subject.ysometsänhoito
dc.subject.ysomatemaattiset mallit
dc.subject.ysobayesilainen menetelmä
dc.subject.ysoregeneraatio (biologia)
dc.subject.ysopuusto
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13075
jyx.subject.urihttp://www.yso.fi/onto/yso/p6361
jyx.subject.urihttp://www.yso.fi/onto/yso/p7534
jyx.subject.urihttp://www.yso.fi/onto/yso/p11401
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p17818
jyx.subject.urihttp://www.yso.fi/onto/yso/p13847
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s10651-021-00514-3
jyx.fundinginformationOpen access funding provided by Natural Resources Institute Finland (LUKE). MK, MM and MV were financially supported by the Academy of Finland (Project Numbers 306875, 327211, 295100 and 315619) and AS by the Swedish Research Council (VR 2018-03986).
dc.type.okmA1


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