Approximate Bayesian Conditional Copulas
dc.contributor.author | Grazian, C | |
dc.contributor.author | Dalla Valle, Luciana | |
dc.contributor.author | Liseo, B | |
dc.date.accessioned | 2022-01-11T17:55:15Z | |
dc.date.issued | 2022-05 | |
dc.identifier.issn | 0167-9473 | |
dc.identifier.issn | 1872-7352 | |
dc.identifier.other | 107417 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/18544 | |
dc.description.abstract |
Copula models are flexible tools to represent complex structures of dependence for multivariate random variables. According to Sklar's theorem, any multidimensional absolutely continuous distribution function can be uniquely represented as a copula, i.e. a joint cumulative distribution function on the unit hypercube with uniform marginals, which captures the dependence structure among the vector components. In real data applications, the interest of the analyses often lies on specific functionals of the dependence, which quantify aspects of it in a few numerical values. A broad literature exists on such functionals, however extensions to include covariates are still limited. This is mainly due to the lack of unbiased estimators of the conditional copula, especially when one does not have enough information to select the copula model. Several Bayesian methods to approximate the posterior distribution of functionals of the dependence varying according covariates are presented and compared; the main advantage of the investigated methods is that they use nonparametric models, avoiding the selection of the copula, which is usually a delicate aspect of copula modelling. These methods are compared in simulation studies and in two realistic applications, from civil engineering and astrophysics. | |
dc.format.extent | 107417-107417 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier | |
dc.subject | Approximate Bayesian computation | |
dc.subject | Bayesian inference | |
dc.subject | Dependence modelling | |
dc.subject | Gaussian processes | |
dc.subject | Empirical likelihood | |
dc.subject | Splines | |
dc.title | Approximate Bayesian Conditional Copulas | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000751459600006&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.volume | 169 | |
plymouth.publication-status | Published | |
plymouth.journal | Computational Statistics and Data Analysis | |
dc.identifier.doi | 10.1016/j.csda.2021.107417 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
dcterms.dateAccepted | 2021-12-30 | |
dc.rights.embargodate | 2023-1-14 | |
dc.identifier.eissn | 1872-7352 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.funder | Royal Society | |
rioxxterms.identifier.project | High-Dimensional Bayesian Dependence Modelling with Conditional Copulas | |
rioxxterms.versionofrecord | 10.1016/j.csda.2021.107417 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2022-05 | |
rioxxterms.type | Journal Article/Review | |
plymouth.funder | High-Dimensional Bayesian Dependence Modelling with Conditional Copulas::Royal Society |