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- Publisher Website: 10.1080/10618600.2022.2157835
- Scopus: eid_2-s2.0-85147680401
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Article: Dependence Model Assessment and Selection with DecoupleNets
Title | Dependence Model Assessment and Selection with DecoupleNets |
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Authors | |
Keywords | Copulas Graphical approach Model assessment Model selection Neural networks Rosenblatt transformation |
Issue Date | 2023 |
Citation | Journal of Computational and Graphical Statistics, 2023 How to Cite? |
Abstract | Neural networks are suggested for learning a map from d-dimensional samples with any underlying dependence structure to multivariate uniformity in (Formula presented.) dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for (Formula presented.) is Rosenblatt’s transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to (Formula presented.), in particular (Formula presented.). This allows for simpler model assessment and selection, both numerically and, because (Formula presented.), especially graphically. A graphical assessment method has the advantage of being able to identify why, or in which region of the domain, a candidate model does not provide an adequate fit, thus, leading to model selection in particular regions of interest or improved model building strategies in such regions. Through simulation studies with data from various copulas, the feasibility and validity of this novel DecoupleNet approach is demonstrated. Applications to real world data illustrate its usefulness for model assessment and selection. Supplementary materials for this article are available online. |
Persistent Identifier | http://hdl.handle.net/10722/325598 |
ISSN | 2023 Impact Factor: 1.4 2023 SCImago Journal Rankings: 1.530 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hofert, Marius | - |
dc.contributor.author | Prasad, Avinash | - |
dc.contributor.author | Zhu, Mu | - |
dc.date.accessioned | 2023-02-27T07:34:39Z | - |
dc.date.available | 2023-02-27T07:34:39Z | - |
dc.date.issued | 2023 | - |
dc.identifier.citation | Journal of Computational and Graphical Statistics, 2023 | - |
dc.identifier.issn | 1061-8600 | - |
dc.identifier.uri | http://hdl.handle.net/10722/325598 | - |
dc.description.abstract | Neural networks are suggested for learning a map from d-dimensional samples with any underlying dependence structure to multivariate uniformity in (Formula presented.) dimensions. This map, termed DecoupleNet, is used for dependence model assessment and selection. If the data-generating dependence model was known, and if it was among the few analytically tractable ones, one such transformation for (Formula presented.) is Rosenblatt’s transform. DecoupleNets have multiple advantages. For example, they only require an available sample and are applicable to (Formula presented.), in particular (Formula presented.). This allows for simpler model assessment and selection, both numerically and, because (Formula presented.), especially graphically. A graphical assessment method has the advantage of being able to identify why, or in which region of the domain, a candidate model does not provide an adequate fit, thus, leading to model selection in particular regions of interest or improved model building strategies in such regions. Through simulation studies with data from various copulas, the feasibility and validity of this novel DecoupleNet approach is demonstrated. Applications to real world data illustrate its usefulness for model assessment and selection. Supplementary materials for this article are available online. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Computational and Graphical Statistics | - |
dc.subject | Copulas | - |
dc.subject | Graphical approach | - |
dc.subject | Model assessment | - |
dc.subject | Model selection | - |
dc.subject | Neural networks | - |
dc.subject | Rosenblatt transformation | - |
dc.title | Dependence Model Assessment and Selection with DecoupleNets | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1080/10618600.2022.2157835 | - |
dc.identifier.scopus | eid_2-s2.0-85147680401 | - |
dc.identifier.eissn | 1537-2715 | - |
dc.identifier.isi | WOS:000926277300001 | - |