Show simple item record

Bayesian Inference for Partially Observed Branching Processes

dc.contributor.authorDonnet, Sophie
HAL ID: 14568
ORCID: 0000-0003-4370-7316
dc.contributor.authorRousseau, Judith
dc.date.accessioned2013-01-17T08:28:00Z
dc.date.available2013-01-17T08:28:00Z
dc.date.issued2016
dc.identifier.issn1931-6690
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/10850
dc.language.isoenen
dc.subjectCounting Process
dc.subjectBayesian analysis
dc.subjectBranching process
dc.subjectLatent variables
dc.subject.ddc519en
dc.subject.classificationjelC11en
dc.titleBayesian Inference for Partially Observed Branching Processes
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherCentre de Recherche en Économie et Statistique (CREST) http://www.crest.fr/ INSEE – École Nationale de la Statistique et de l'Administration Économique;France
dc.description.abstractenPoisson processes are used in various application fields applications (public health biology, reliability and so on). In their homogeneous version, the intensity process is a deterministic constant. In their inhomogeneous version, it depends on time. To allow for an endogenous evolution of the intensity process we consider multiplicative intensity processes. Inference methods have been developed when the trajectories are fully observed. We deal with the case of a partially observed process. As a motivating example, consider the analysis of an electrical network through time. This network is composed of cables and accessories (joints). When a cable fails, the cable is replaced by a new cable connected to the network by two new accessories. When an accessory fails, the same kind of reparation is done leading to the addition of only one accessory. The failure rate depends on the stochastically evolving number of accessories. We only observe the times events; the initial number of accessories and the cause of the incident (cable or accessory) are only partially observed. The aim is to estimate the different failure rates or to make predictions. The inference is strongly influenced by the initial number of accessories, which is typically an unknown quantity. We deduce a sensible prior on the initial number of accessories using the probabilistic properties of the process . We illustrate the performances of our methodology on a large simulation study.
dc.relation.isversionofjnlnameBayesian Analysis
dc.relation.isversionofjnlvol11
dc.relation.isversionofjnlissue1
dc.relation.isversionofjnldate2016
dc.relation.isversionofjnlpages151-190
dc.relation.isversionofdoihttp://dx.doi.org/10.1214/15-BA940
dc.identifier.urlsitehttp://dx.doi.org/10.1214/15-BA940
dc.relation.isversionofjnlpublisherPergamon Press
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingprintoui
dc.description.submittednonen
dc.description.ssrncandidatenon
dc.description.halcandidateoui
dc.description.readershiprecherche
dc.description.audienceInternational
dc.relation.Isversionofjnlpeerreviewedoui
dc.date.updated2016-09-24T13:51:27Z


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