Show simple item record

dc.contributor.authorArbel, Julyan
dc.contributor.authorMengersen, Kerrie
dc.contributor.authorRousseau, Judith
dc.date.accessioned2014-02-26T15:34:45Z
dc.date.available2014-02-26T15:34:45Z
dc.date.issued2016
dc.identifier.issn1932-6157
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/12764
dc.language.isoenen
dc.subjectStick-breaking representation
dc.subjectGriffiths-Engen-McCloskey distribution
dc.subjectGaussian process
dc.subjectCovariate-dependent model
dc.subjectBayesian nonparametrics
dc.subject.ddc519en
dc.titleBayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherQueensland University of Technology;Australie
dc.contributor.editoruniversityotherCollegio Carlo Alberto;Italie
dc.description.abstractenWe introduce a dependent Bayesian nonparametric model for the probabilistic modeling of membership of subgroups in a community based on partially replicated data. The focus here is on species-by-site data, that is, community data where observations at different sites are classified in distinct species. Our aim is to study the impact of additional covariates, for instance, environmental variables, on the data structure, and in particular on the community diversity. To this end, we introduce dependence a priori across the covariates and show that it improves posterior inference. We use a dependent version of the Griffiths–Engen–McCloskey distribution defined via the stick-breaking construction. This distribution is obtained by transforming a Gaussian process whose covariance function controls the desired dependence. The resulting posterior distribution is sampled by Markov chain Monte Carlo. We illustrate the application of our model to a soil microbial data set acquired across a hydrocarbon contamination gradient at the site of a fuel spill in Antarctica. This method allows for inference on a number of quantities of interest in ecotoxicology, such as diversity or effective concentrations, and is broadly applicable to the general problem of community response to environmental variables.
dc.publisher.cityParisen
dc.relation.isversionofjnlnameThe Annals of Applied Statistics
dc.relation.isversionofjnlvol10
dc.relation.isversionofjnlissue3
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpages1496-1516
dc.relation.isversionofdoi10.1214/16-AOAS944
dc.identifier.urlsitehttps://arxiv.org/abs/1402.3093v2
dc.relation.isversionofjnlpublisherThe Institute of Mathematical Statistics
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.description.submittednonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2017-01-16T11:16:08Z


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record