Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17819
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dc.contributor.authorTosetti, E-
dc.contributor.authorVinciotti, V-
dc.date.accessioned2019-04-01T08:58:45Z-
dc.date.available2019-04-01T08:58:45Z-
dc.date.issued2019-
dc.identifier.citationJournal of the Royal Statistical Society: Series Cen_US
dc.identifier.issn0035-9254-
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/17819-
dc.description.sponsorshipThe authors acknowledge nancial support from EPSRC [EP/L021250/1].en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.subjectmixed probiten_US
dc.subjectgraphical modellingen_US
dc.subjectEM algorithmen_US
dc.subjectcredit risk modellingen_US
dc.titleA computationally efficient correlated mixed probit model for credit risk inferenceen_US
dc.typeArticleen_US
dc.relation.isPartOfJournal of the Royal Statistical Society: Series C-
pubs.publication-statusAccepted-
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