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Article: Smooth bootstrapping of copula functionals
Title | Smooth bootstrapping of copula functionals |
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Authors | |
Keywords | bandwidth matrix bandwidth selection data augmentation dependence distortion kernel distribution estima-tion kernel smoothing Smooth bootstrap |
Issue Date | 2022 |
Citation | Electronic Journal of Statistics, 2022, v. 16, n. 1, p. 2550-2606 How to Cite? |
Abstract | The smooth bootstrap for estimating copula functionals in small samples is investigated. It can be used both to gauge the distribution of the estimator in question and to augment the data. Issues arising from kernel density and distribution estimation in the copula domain are addressed, such as how to avoid the bounded domain, which bandwidth matrix to choose, and how the smoothing can be carried out. Furthermore, we in-vestigate how the smooth bootstrap impacts the underlying dependence structure or the functionals in question and under which conditions it does not. We provide specific examples and simulations that highlight advan-tages and caveats of the approach. |
Persistent Identifier | http://hdl.handle.net/10722/325643 |
ISSN | 2023 Impact Factor: 1.0 2023 SCImago Journal Rankings: 1.256 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Coblenz, Maximilian | - |
dc.contributor.author | Grothe, Oliver | - |
dc.contributor.author | Herrmann, Klaus | - |
dc.contributor.author | Hofert, Marius | - |
dc.date.accessioned | 2023-02-27T07:35:00Z | - |
dc.date.available | 2023-02-27T07:35:00Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Electronic Journal of Statistics, 2022, v. 16, n. 1, p. 2550-2606 | - |
dc.identifier.issn | 1935-7524 | - |
dc.identifier.uri | http://hdl.handle.net/10722/325643 | - |
dc.description.abstract | The smooth bootstrap for estimating copula functionals in small samples is investigated. It can be used both to gauge the distribution of the estimator in question and to augment the data. Issues arising from kernel density and distribution estimation in the copula domain are addressed, such as how to avoid the bounded domain, which bandwidth matrix to choose, and how the smoothing can be carried out. Furthermore, we in-vestigate how the smooth bootstrap impacts the underlying dependence structure or the functionals in question and under which conditions it does not. We provide specific examples and simulations that highlight advan-tages and caveats of the approach. | - |
dc.language | eng | - |
dc.relation.ispartof | Electronic Journal of Statistics | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | bandwidth matrix | - |
dc.subject | bandwidth selection | - |
dc.subject | data augmentation | - |
dc.subject | dependence distortion | - |
dc.subject | kernel distribution estima-tion | - |
dc.subject | kernel smoothing | - |
dc.subject | Smooth bootstrap | - |
dc.title | Smooth bootstrapping of copula functionals | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1214/22-EJS2007 | - |
dc.identifier.scopus | eid_2-s2.0-85129328921 | - |
dc.identifier.volume | 16 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 2550 | - |
dc.identifier.epage | 2606 | - |
dc.identifier.isi | WOS:000825293500050 | - |