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dc.contributor.authorLey, Christophe
dc.contributor.authorBabic, Sladana
dc.contributor.authorCraens, Domien
dc.date.accessioned2021-02-04T15:06:21Z
dc.date.available2021-02-04T15:06:21Z
dc.date.issued2021en_US
dc.identifier.doi10.1146/annurev-statistics-040720-025210
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6646
dc.description.abstractProbability distributions are the building blocks of statistical modeling and inference. It is therefore of the utmost importance to know which distribution to use in what circumstances, as wrong choices will inevitably entail a biased analysis. In this article, we focus on circumstances involving complex data and describe the most popular flexible models for these settings. We focus on the following complex data: multivariate skew and heavy-tailed data, circular data, toroidal data, and cylindrical data. We illustrate the strength of flexible models on the basis of concrete examples and discuss major applications and challenges.en_US
dc.language.isoenen_US
dc.publisherAnnual Reviewsen_US
dc.subjectDistributionen_US
dc.titleFlexible models for complex data with applicationsen_US
dc.identifier.journalAnnual Review of Statistics and Its Applicationen_US
dc.source.volume8en_US
dc.source.issueMarchen_US
dc.contributor.departmentDepartment of Applied Mathematics, Computer Science and Statistics, Ghent University, Gent, B-9000 Belgiumen_US
dc.identifier.eissn2326-831X
vlerick.knowledgedomainAccounting & Financeen_US
vlerick.typearticleJournal article with impact factoren_US
vlerick.vlerickdepartmentAFen_US
dc.identifier.vperid240544en_US


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