Näytä suppeat kuvailutiedot

dc.contributor.authorZito, Alessandro
dc.contributor.authorRigon, Tommaso
dc.contributor.authorOvaskainen, Otso
dc.contributor.authorDunson, David B.
dc.date.accessioned2022-11-28T08:11:55Z
dc.date.available2022-11-28T08:11:55Z
dc.date.issued2022
dc.identifier.citationZito, A., Rigon, T., Ovaskainen, O., & Dunson, D. B. (2022). Bayesian Modeling of Sequential Discoveries. <i>Journal of the American Statistical Association</i>, <i>Early online</i>. <a href="https://doi.org/10.1080/01621459.2022.2060835" target="_blank">https://doi.org/10.1080/01621459.2022.2060835</a>
dc.identifier.otherCONVID_117772805
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/84100
dc.description.abstractWe aim at modelling the appearance of distinct tags in a sequence of labelled objects. Common examples of this type of data include words in a corpus or distinct species in a sample. These sequential discoveries are often summarised via accumulation curves, which count the number of distinct entities observed in an increasingly large set of objects. We propose a novel Bayesian method for species sampling modelling by directly specifying the probability of a new discovery, therefore allowing for flexible specifications. The asymptotic behavior and finite sample properties of such an approach are extensively studied. Interestingly, our enlarged class of sequential processes includes highly tractable special cases. We present a subclass of models characterized by appealing theoretical and computational properties, including one that shares the same discovery probability with the Dirichlet process. Moreover, due to strong connections with logistic regression models, the latter subclass can naturally account for covariates. We finally test our proposal on both synthetic and real data, with special emphasis on a large fungal biodiversity study in Finland.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.ispartofseriesJournal of the American Statistical Association
dc.rightsCC BY-NC-ND 4.0
dc.subject.otheraccumulation curves
dc.subject.otherdirichlet process
dc.subject.otherlogistic regression
dc.subject.otherpoisson-binomial distribution
dc.subject.otherspecies sampling models
dc.titleBayesian Modeling of Sequential Discoveries
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202211285379
dc.contributor.laitosBio- ja ympäristötieteiden laitosfi
dc.contributor.laitosDepartment of Biological and Environmental Scienceen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0162-1459
dc.relation.volumeEarly online
dc.type.versionacceptedVersion
dc.rights.copyright© 2022 American Statistical Association
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber856506
dc.relation.grantnumber856506
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/856506/EU//LIFEPLAN
dc.subject.ysotilastolliset mallit
dc.subject.ysobayesilainen menetelmä
dc.subject.ysolajistokartoitus
dc.subject.ysootanta
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p26278
jyx.subject.urihttp://www.yso.fi/onto/yso/p17803
jyx.subject.urihttp://www.yso.fi/onto/yso/p29383
jyx.subject.urihttp://www.yso.fi/onto/yso/p12939
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1080/01621459.2022.2060835
dc.relation.funderEuropean Commissionen
dc.relation.funderEuroopan komissiofi
jyx.fundingprogramERC European Research Council, H2020en
jyx.fundingprogramERC European Research Council, H2020fi
jyx.fundinginformationThis project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 856506).
dc.type.okmA1


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