New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation

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dc.contributor.authorAlonso Revenga, Juana María
dc.contributor.authorMartín Apaolaza, Nirian
dc.contributor.authorPardo Llorente, Leandro
dc.contributor.editorBrugnano, Luigi
dc.contributor.editorEfendiev, Yalchin
dc.contributor.editorKeller, André
dc.contributor.editorKwok-Po, Michael
dc.contributor.editorRomani, Lucia
dc.contributor.editorTank. Fatih
dc.date.accessioned2024-05-29T10:51:25Z
dc.date.available2024-05-29T10:51:25Z
dc.date.issued2020-08-15
dc.description.abstractTraditionally, the Dirichlet-multinomial distribution has been recognized as a key model for contingency tables generated by cluster sampling schemes. There are, however, other possible distributions appropriate for these contingency tables. This paper introduces new statistics capable of testing log-linear modeling hypotheses with distributional unspecification, when the individuals of the clusters are possibly homogeneously correlated. An estimator for the intracluster correlation coefficient, valid for different cluster sizes, plays a crucial role in the construction of the goodness-of-fit test-statistics.
dc.description.departmentDepto. de Estadística y Ciencia de los Datos
dc.description.facultyFac. de Estudios Estadísticos
dc.description.refereedTRUE
dc.description.sponsorshipMinisterio de Ciencia, Innovación y Universidades
dc.description.statuspub
dc.identifier.citationAlonso-Revenga, Martín y Pardo (2020) «New statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation», Journal of Computational and Applied Mathematics, 374.
dc.identifier.doi10.1016/J.CAM.2020.112757
dc.identifier.essn1879-1778
dc.identifier.issn0377-0427
dc.identifier.officialurlhttps://doi.org/10.1016/j.cam.2020.112757
dc.identifier.relatedurlhttps://www.sciencedirect.com/science/article/pii/S0377042720300480?via%3Dihub
dc.identifier.urihttps://hdl.handle.net/20.500.14352/104517
dc.journal.titleJournal of Computational and Applied Mathematics
dc.language.isoeng
dc.page.initial112757
dc.publisherElsevier
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.accessRightsrestricted access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.cdu519.235
dc.subject.cdu004.415.26
dc.subject.keywordClustered multinomial data
dc.subject.keywordConsistent intracluster correlation
dc.subject.keywordEstimator
dc.subject.keywordLog-linear model
dc.subject.keywordOverdispersion
dc.subject.keywordQuasi minimum divergence estimator
dc.subject.ucmProbabilidades (Estadística)
dc.subject.unesco1208 Probabilidad
dc.titleNew statistics to test log-linear modeling hypothesis with no distributional specifications and clusters with homogeneous correlation
dc.typejournal article
dc.type.hasVersionVoR
dc.volume.number374
dspace.entity.typePublication
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