Bayesian Nonparametric Modelling of Conditional Multidimensional Dependence Structures
dc.contributor.author | Barone, R | |
dc.contributor.author | Dalla Valle, Luciana | |
dc.date.accessioned | 2023-01-19T12:47:32Z | |
dc.date.issued | 2023-01-30 | |
dc.identifier.issn | 1537-2715 | |
dc.identifier.issn | 1537-2715 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/20188 | |
dc.description.abstract |
In recent years, conditional copulas, that allow dependence between variables to vary according to the values of one or more covariates, have attracted increasing attention. However, the literature mainly focused on the bivariate case, since the constraints on the multivariate copulas correlation matrices would make the specifications of covariates arduous. In high dimension, vine copulas offer greater flexibility compared to multivariate copulas, since they are constructed using bivariate copulas as building blocks. We present a novel inferential approach for multivariate distributions, which combines the flexibility of vine constructions with the advantages of Bayesian nonparametrics, not requiring the specification of parametric families for each pair copula. Expressing multivariate copulas using vines allows us to easily account for covariate specifications driving the dependence between response variables. We specify the vine copula density as an infinite mixture of Gaussian copulas, defining a Dirichlet process prior on the mixing measure, and performing posterior inference via Markov chain Monte Carlo sampling. Our approach is successful as for clustering as well as for density estimation. We carry out simulation studies and apply the proposed approach to analyse a veterinary dataset and to investigate the impact of natural disasters on financial development. Supplementary materials are available online. | |
dc.format.extent | 1-10 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | American Statistical Association | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject | Conditional copulas | |
dc.subject | Dirichlet process prior | |
dc.subject | Heterogeneity | |
dc.subject | MCMC | |
dc.subject | Mixtures | |
dc.subject | Vine copulas | |
dc.title | Bayesian Nonparametric Modelling of Conditional Multidimensional Dependence Structures | |
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:000943960100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | ahead-of-print | |
plymouth.volume | ahead-of-print | |
plymouth.publication-status | Published online | |
plymouth.journal | Journal of Computational and Graphical Statistics | |
dc.identifier.doi | 10.1080/10618600.2023.2173604 | |
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 | 2023-01-18 | |
dc.rights.embargodate | 2024-1-30 | |
dc.identifier.eissn | 1537-2715 | |
rioxxterms.funder | Engineering and Physical Sciences Research Council | |
rioxxterms.identifier.project | Dependence Modelling with Vine Copulas for the Integration of Unstructured and Structured Data | |
rioxxterms.versionofrecord | 10.1080/10618600.2023.2173604 | |
rioxxterms.licenseref.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
rioxxterms.type | Journal Article/Review | |
plymouth.funder | Dependence Modelling with Vine Copulas for the Integration of Unstructured and Structured Data::Engineering and Physical Sciences Research Council |