Nonparametric estimation of copula functions for dependence modelling Chen, Song Huang, Tzeeming
dc.contributor.department Statistics 2018-02-16T19:07:18.000 2020-07-02T06:56:11Z 2020-07-02T06:56:11Z 2005-11-01
dc.description.abstract <p>Copulas are full measures of dependence among components of random vectors. Unlike the marginal and the joint distributions which are directly observable, a copula is a hidden dependence structure that couples the marginals and the joint distribution. This makes the task of proposing a parametric copula model non-trivial and is where a nonparametric estimator can play a significant role. In this paper, we investigate a kernel estimator which is mean square consistent everywhere in the support of the copula function. The kernel estimator is then used to formulate a goodness-of-fit test for parametric copula models.</p>
dc.description.comments <p>This preprint was published as Song Xi Chen and Tzee-Ming Huang, "Nonparametric Estimation of Copula Functions for Dependence Modelling", <em>Canadian Journal of Statistics</em> (2009): 265-282, doi: <a href="" target="_blank">10.1002/cjs.5550350205</a></p>
dc.identifier archive/
dc.identifier.articleid 1042
dc.identifier.contextkey 7331864
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_preprints/42
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 00:12:18 UTC 2022
dc.source.uri 10.1002/cjs.5550350205
dc.subject.disciplines Statistics and Probability
dc.subject.keywords dependence modeling
dc.subject.keywords goodness-of-fit tests
dc.subject.keywords kernel estimator
dc.subject.keywords parametric copula models
dc.title Nonparametric estimation of copula functions for dependence modelling
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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