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dc.contributor.authorGijbels, Irène
dc.contributor.authorVeraverbeke, Noël
dc.contributor.authorOmelka, Marek
dc.date.accessioned2014-01-17T07:26:56Z
dc.date.available2014-01-17T07:26:56Z
dc.date.issued2012
dc.identifier.citationGijbels, I. et al. 2012. Multivariate and functional covariates and conditional copulas. Electronic journal of statistics, 6:1273-1306. [http://projecteuclid.org/euclid.ejs]en_US
dc.identifier.issn1935-7524
dc.identifier.urihttp://hdl.handle.net/10394/9957
dc.identifier.urihttp://dx.doi.org/10.1214/12-EJS712
dc.identifier.urihttps://projecteuclid.org/download/pdfview_1/euclid.ejs/1343310298
dc.description.abstractIn this paper the interest is to estimate the dependence between two variables conditionally upon a covariate, through copula modelling. In recent literature nonparametric estimators for conditional copula functions in case of a univariate covariate have been proposed. The aim of this paper is to nonparametrically estimate a conditional copula when the covariate takes on values in more complex spaces. We consider multivariate covariates and functional covariates. We establish weak convergence, and bias and variance properties of the proposed nonparametric estimators. We also briefly discuss nonparametric estimation of conditional association measures such as a conditional Kendall’s tau. The case of functional covariates is of particular interest and challenge, both from theoretical as well as practical point of view. For this setting we provide an illustration with a real data example in which the covariates are spectral curves. A simulation study investigating the finite-sample performances of the discussed estimators is provided.en_US
dc.description.abstractIn this paper the interest is to estimate the dependence between two variables conditionally upon a covariate, through copula modelling. In recent literature nonparametric estimators for conditional copula functions in case of a univariate covariate have been proposed. The aim of this paper is to nonparametrically estimate a conditional copula when the covariate takes on values in more complex spaces. We consider multivariate covariates and functional covariates. We establish weak convergence, and bias and variance properties of the proposed nonparametric estimators.We also briefly discuss nonparametric estimation of conditional association measures such as a conditional Kendall’s tau. The case of functional covariates is of particular interest and challenge, both from theoretical as well as practical point of view. For this setting we provide an illustration with a real data example in which the covariates are spectral curves. A simulation study investigating the finite-sample performances of the discussed estimators is provided
dc.language.isoenen_US
dc.publisherInstitute of Mathematical Statisticsen_US
dc.subjectAsymptotic representationen_US
dc.subjectempirical copula processen_US
dc.subjectfunctional covariatesen_US
dc.subjectmultivariate covariatesen_US
dc.subjectsmall ball probabilityen_US
dc.subjectrandom designen_US
dc.subjectsmoothingen_US
dc.titleMultivariate and functional covariates and conditional copulasen_US
dc.typeArticleen_US
dc.contributor.researchID22051880 - Veraverbeke, Noël Daniel


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