Publication:
Bayesian analysis of multiple-inflation poisson models and its application to infection data

dc.contributor.coauthorRyu, Duchwan
dc.contributor.coauthorBilgili, Devrim
dc.contributor.coauthorEbrahimi, Nader
dc.contributor.departmentKUH (Koç University Hospital)
dc.contributor.departmentSchool of Medicine
dc.contributor.facultymemberYes
dc.contributor.kuauthorErgönül, Önder
dc.contributor.schoolcollegeinstituteKUH (KOÇ UNIVERSITY HOSPITAL)
dc.contributor.schoolcollegeinstituteSCHOOL OF MEDICINE
dc.date.accessioned2024-11-09T23:50:11Z
dc.date.issued2018
dc.description.abstractIn this article we propose a multiple-inflation Poisson regression to model count response data containing excessive frequencies at more than one non-negative integer values. To handle multiple excessive count responses, we generalize the zero-inflated Poisson regression by replacing its binary regression with the multinomial regression, while Su et al. [Statist. Sinica 23 (2013) 1071-1090] proposed a multiple-inflation Poisson model for consecutive count responses with excessive frequencies. We give several properties of our proposed model, and do statistical inference under the fully Bayesian framework. We perform simulation studies and also analyze the data related to the number of infections collected in five major hospitals in Turkey, using our methodology.
dc.description.fulltextNo
dc.description.harvestedfromManual
dc.description.indexedbyWOS
dc.description.indexedbyScopus
dc.description.openaccessYES
dc.description.peerreviewstatusPeer-Reviewed
dc.description.publisherscopeInternational
dc.description.readpublishN/A
dc.description.sponsoredbyTubitakEuN/A
dc.description.sponsorshipNational Science Foundation [DMS-12-08273] We thank the referees and Associate Editor for their helpful comments that led to improvement of our paper. Ebrahimi's research was partially supported by National Science Foundation, DMS-12-08273.
dc.description.studentonlypublicationNo
dc.description.studentpublicationNo
dc.description.versionN/A
dc.identifier.doi10.1214/16-BJPS340
dc.identifier.embargoN/A
dc.identifier.endpage261
dc.identifier.issn0103-0752
dc.identifier.issue2
dc.identifier.quartileQ4
dc.identifier.scopus2-s2.0-85045514436
dc.identifier.startpage239
dc.identifier.urihttps://doi.org/10.1214/16-BJPS340
dc.identifier.urihttps://hdl.handle.net/20.500.14288/14494
dc.identifier.volume32
dc.identifier.wos000430260100002
dc.keywordsBayesian generalized linear model
dc.keywordsEM algorithm
dc.keywordsexcessive count response
dc.keywordslikelihood function
dc.keywordszero-inflated poisson model
dc.keywordsCount Data
dc.language.isoeng
dc.publisherBrazilian Statistical Association
dc.relation.affiliationKoç University
dc.relation.collectionKoç University Institutional Repository
dc.relation.ispartofBrazilian Journal of Probability and Statistics
dc.relation.openaccessN/A
dc.rightsN/A
dc.subjectStatistics
dc.subjectProbability
dc.titleBayesian analysis of multiple-inflation poisson models and its application to infection data
dc.typeJournal Article
dspace.entity.typePublication
local.contributor.kuauthorErgönül, Mehmet Önder
relation.isOrgUnitOfPublicationf91d21f0-6b13-46ce-939a-db68e4c8d2ab
relation.isOrgUnitOfPublicationd02929e1-2a70-44f0-ae17-7819f587bedd
relation.isOrgUnitOfPublication.latestForDiscoveryf91d21f0-6b13-46ce-939a-db68e4c8d2ab
relation.isParentOrgUnitOfPublication055775c9-9efe-43ec-814f-f6d771fa6dee
relation.isParentOrgUnitOfPublication17f2dc8e-6e54-4fa8-b5e0-d6415123a93e
relation.isParentOrgUnitOfPublication.latestForDiscovery055775c9-9efe-43ec-814f-f6d771fa6dee

Files